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Abstract: Good cross-directional control of sheet variables in a paper machine depends on a number of factors. One such factor is the correct alignment of actuators with their corresponding responses. The alignment changes with time either locally or across the whole paper sheet due to a variety of reasons. In this paper, we provide an online mapping technique based on estimating the cross-correlation between actuator movement and the measurements. This procedure involves two steps–in the first step (called alignment monitoring) we determine if the current mapping is wrong at any CD position, and in the second step (called re-mapping), the alignment of actuators is corrected. The main advantage of this method is in its ability to re-align under closed loop conditions with minimal operational disruption. [Copyright &y& Elsevier]

Abstract: The purpose of this paper is to present a novel technique for analyzing the behavior of an industrial system stochastically by utilizing vague, imprecise, and uncertain data. In the present study two important tools namely Lambda-Tau methodology and particle swarm optimization are combinedly used to present a novel technique named as particle swarm optimization based Lambda-Tau (PSOBLT) for analyzing the behavior of a complex repairable system stochastically up to a desired degree of accuracy. Expressions of reliability indices like failure rate, repair time, mean time between failures (MTBF), expected number of failures (ENOF), reliability and availability for the system are obtained by using Lambda-Tau methodology and particle swarm optimization is used to construct their membership function. The interaction among the working units of the system is modeled with the help of Petri nets. The feeding unit of a paper mill situated in a northern part of India, producing approximately 200ton of paper per day, has been considered to demonstrate the proposed approach. Sensitivity analysis of system''s behavior has also been done. The behavior analysis results computed by PSOBLT technique have a reduced region of prediction in comparison of existing technique region, i.e. uncertainties involved in the analysis are reduced. Thus, it may be a more useful analysis tool to assess the current system conditions and involved uncertainties. [Copyright &y& Elsevier]

This paper proposes a differential privacy decentralized zeroth-order gradient tracking optimization (DP-DZOGT) algorithm for solving optimization problems of decentralized systems, where the gradient information of the function is unknown. To address the challenge of unknown gradient information, a one-point zeroth-order gradient estimator (OPZOGE) is constructed, which can estimate the gradient based on the function value and guide the update of decision variables. Additionally, to prevent privacy leakage of agents, random noise is introduced into both the state and the gradient of the agents, which effectively enhances the level of privacy protection. The linear convergence of the proposed DP-DZOGT under a fixed step size can be guaranteed. Moreover, it has been applied to the fields of smart grid (SG) and decentralized federated learning (DFL). Finally, the effectiveness of the algorithm is validated through three numerical simulations. • This paper proposes a differential privacy decentralized zeroth-order gradient tracking optimization algorithm. • The proposed algorithm not only protects data privacy but also effectively improves convergence speed. • This paper explores the practical applications of DP-DZOGT in the domains of smart grids and decentralized federated learning. [ABSTRACT FROM AUTHOR]

This paper addresses the event-triggered prescribed-time control problem for a class of high-order nonlinear systems based on finite time-varying gain. Due to the existence of unknown gain functions and system uncertainties, the resultant control with prescribed-time convergence performance becomes nontrivial. The problem becomes even more complex due to the use of event-based communication instead of continuous communication. To tackle the aforementioned challenges, this paper proposes an event-triggered prescribed-time stabilization approach with the following key steps. Firstly, we establish a new prescribed-time stability lemma to overcome the technical difficulty arising from the prescribed-time controller design, and stability analysis. Secondly, we give the controller design procedure upon using the backstepping technique. Thirdly, we redesign the event-triggering threshold condition based on time-varying functions, which allows the controller terminal jitter to be mitigated and the controller to be implemented straightforwardly in practice. Furthermore, the proposed control scheme avoids Zeno behavior. The numerical simulation confirms the effectiveness of the proposed control scheme. • Development of a new prescribed-time stability lemma for complex nonlinear system. • Utilization of finite time-varying gain in the prescribed-time control strategy. • The proposed event-triggering condition, significantly reducing controller jitter. • Successful avoidance of Zeno behavior in the prescribed-time control scheme. [ABSTRACT FROM AUTHOR]

Fault detection and diagnosis of nonstationary processes are crucial for ensuring the safety of industrial production systems. However, the nonstationarity of process data poses multifaceted challenges to them. First, conventional stationary fault detection methods encounter difficulties in discerning evolving trends within nonstationary data. Secondly, the majority of current nonstationary fault detection methods directly extract features from all variables, rendering them susceptible to redundant interference. Moreover, nonstationary trends possess the capacity to conceal and modify the correlations among variables. Coupled with the smearing effect of faults, it is challenging to achieve accurate fault diagnosis. To address these challenges, this paper proposes sparse Wasserstein stationary subspace analysis (SWSSA). Specifically, a ℓ 2 , p -norm constraint is introduced to endow the stationary subspace model with excellent sparse representation capability. Furthermore, recognizing that fault variables within the sparse stationary subspace influence only a limited subset of stationary sources, this paper proposes a novel contribution analysis method based on local dynamic preserving projection (LDPP), termed LDPPBC, which can effectively mitigate the smearing effect on nonstationary fault diagnosis. LDPPBC establishes a LDPP matrix by extracting the latent positional information of fault variables within the stationary subspace. This allows LDPPBC to selectively analyze the contributions of variables within the latent fault subspace to achieve precise fault diagnosis while avoiding the interference of variable contributions from the fault-free subspace. Finally, the superiority of the proposed method is thoroughly validated through a numerical simulation, a continuous stirred tank reactor, and a real industrial roaster. • This method can realize fault diagnosis under the influence of nonstationarity. • The smearing effect of faults is effectively mitigated. • Through mathematical deduction, the convergence of the algorithm is proven. [ABSTRACT FROM AUTHOR]

In this study, the problem of non-overshooting tracking control (NOTC) for a class of strict-feedback systems with prescribed finite-time (FT) is studied. In order to obtain a simple closed-loop system (CLS), we first propose a criterion lemma for prescribed FT stable control. A prescribed FT controller is designed by using backstepping and the proposed criterion lemma. According to the CLS, many conditions of NOTC are analyzed. Compared with the related results, the NOTC conditions obtained in this paper are relatively extensive, which reduces conservatism. The algorithm makes the system overshoot zero, so this result increases the accuracy. The final simulation results show that this algorithm is effective. • In the related research results of no overshoot control, the closed-loop system obtained by the existing prescribed finite-time control algorithm is very complicated. This leads to the failure to calculate the expression of tracking error through the closed-loop system, and then the control condition with non-overshooting cannot be obtained. Then, a new criterion lemma of prescribed finite-time control is proposed to design the control in this paper. Obviously, our closed-loop system is relatively simple. • In the related research results of non-overshooting control, the non-overshooting tracking control conditions obtained in this paper are relatively extensive, which reduces the conservatism of the existing algorithms. • Compared with mean-nonovershooting tracking control, the algorithm in this paper can make the overshoot of tracking error zero. Therefore, this result increases accuracy. [ABSTRACT FROM AUTHOR]

This research proposes an Optimized Current chopping Control (CCC) approach for SRM drives. The goal is to implement a simple SRM drive that can effectively meet electric vehicle requirements, comprising minimized torque ripple to reduce vibrations and acoustic noises, maximized output torque to enhance vehicle acceleration, and improved efficiency, which contributes to extending the EV's battery life. Therefore, an optimization problem is formulated and solved offline, incorporating a CCC-based SRM drive model. The control variables for this optimization problem are the switching angles of the SRM. A multi-objective function is chosen to combine three performance indices: torque ripple, average torque, and efficiency. The Harris Hawks Optimization (HHO) method is utilized in this paper to solve the optimization problem and find the optimal switching angles based on the selected objective function. HHO demonstrates a strong search capability that can effectively handle the nonlinear magnetization characteristics of SRMs. Constraints on the switching angles are also included in the optimization problem to control the phase current's RMS value and power consumption. The optimized switching angles are applied to a current chopping control (CCC) strategy and an asymmetric half-bridge converter to implement the proposed HHO-based CCC drive. Moreover, to demonstrate the effectiveness of the proposed HHO-based CCC drive, a comparative analysis based on simulations and experimental measurements is presented against other CCC approaches for SRM drives, including modified particle swarm algorithm (MPSO)-based CCC drives and analytical-based CCC drives. • A finite element analysis (FEA) software is utilized to determine the magnetic characteristics of SRMs accurately. • The obtained magnetic characteristics are then verified using a series of indirect experimental measurements to build an accurate four-phase 8/6 SRM model. • The Harris Hawks optimization (HHO) method is employed in this paper to find the optimal switching angles based on the selected objective function. • This research proposes an optimized current chopping control (CCC) approach for SRM drives. The goal is to implement a simple SRM drive that can effectively meet electric vehicle requirements. • The proposed HHO-based CCC drive is validated and compared based on simulations and experimental measurements against other CCC approaches for SRM drives, including modified particle swarm algorithm(MPSO)-based CCC drives and analytical-based CCC drives. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to address the issue of vibration control for a class of Euler–Bernoulli beam systems that are subject to external disturbances and input saturation. The proposed controller differs from other backstepping methods in that it employs a radial basis function (RBF) neural network to accurately estimate boundary disturbances and incorporates the hyperbolic tangent function to ensure input constraints. The nonlinear partial differential equation (PDE) model is initially derived based on Hamilton's principle to capture the dominant dynamic characteristics of the flexible beam. In the framework of the Lyapunov direct approach, an adaptive RBF neural network-based law is subsequently designed to estimate the state-related boundary disturbances. The backstepping approach is then developed to propose sufficient conditions for ensuring the stability and convergence of closed-loop systems subject to input saturation. Finally, the effectiveness and superiority of the proposed methodology are further demonstrated by comparing the simulation results of constrained backstepping controllers. [Display omitted] • The paper addresses the issue of vibration control for Euler–Bernoulli beam models. • This system is susceptible to unknown external disturbances and input saturation. • A constrained boundary controller is developed based on backstepping technology. • An RBF neural network observer is proposed to estimate the boundary disturbances. • The closed-loop stability is established by considering the above constraints. [ABSTRACT FROM AUTHOR]

This paper delves into the stability of time-advance delta fractional order systems, with a specific emphasis on the order range (0 , + ∞) rather than the conventional range (0 , 1). The delta Laplace transform is used to investigate the stability of the suggested system, and a mapping relation ρ = s s + 1 is introduced. The explicit stability condition is provided, articulated in relation to a specific distribution of eigenvalues of the system matrix. To validate this condition, the paper establishes equivalence between the delta difference and the nabla difference. Furthermore, both quantitative and qualitative analyses are conducted on the range of the unstable region. Finally, the correctness of the developed results is validated by three examples. • The time advance delta fractional order system is considered. • The range for the system order is extended from α ∈ (0,1) to α ∈ (0 , + ∞). • The explicit stability condition is developed via the delta Laplace transform. • The evolution trend of stable region with regards to the order is discussed. • The connection between the forward case and the backward case is given. [ABSTRACT FROM AUTHOR]

Abstract: The automatic control of grammage (basis weight) in paper and board production is based upon on-line grammage measurement. Furthermore, the automatic control of other quality variables such as moisture, ash content and coat weight, may rely on the grammage measurement. The integrity of Kr-85 based on-line grammage measurement systems was studied, by performing basic calibrations with traceably calibrated plastic reference standards. The calibrations were performed according to the EN ISO/IEC 17025 standard, which is a requirement for calibration laboratories. The observed relative measurement errors were 3.3% in the first time calibrations at the 95% confidence level. With the traceable basic calibration method, however, these errors can be reduced to under 0.5%, thus improving the integrity of on-line grammage measurements. Also a standardised algorithm, based on the experience from the performed calibrations, is proposed to ease the adjustment of the different grammage measurement systems. The calibration technique can basically be applied to all -radiation based grammage measurements. [Copyright &y& Elsevier]

This paper presents the design of a Lyapunov matrix-based adaptive resilient controller for unmanned marine vehicles (UMVs) under state-dependent sensor attacks, input-dependent thruster attacks, and time delays. Different from the thruster attack model that depends on state information, the thruster attack model studied in this paper is related to control input, that is, the input-dependent thruster attacks. This implies that the designed correction signal is also affected by the attacks. To mitigate the impact of the considered sensor attacks and thruster attacks on UMVs, an adaptive mechanism is employed to estimate the attack factors. Furthermore, a Lyapunov matrix-based complete-type Lyapunov–Krasovskii functional (LKF) is introduced, in which more comprehensive time delay information are considered. Based on this, linear matrix inequality (LMI) method and Jensen's inequality are used to obtain sufficient conditions for the existence of the controller. The proposed controller guarantees that the state errors of UMVs converge asymptotically to zero with the adaptive H ∞ performance index no larger than γ 0. Finally, the efficacy of the proposed approach is verified by simulation results. • A novel Lyapunov matrix-based resilient control strategy is proposed. • Compensate for the impact of the correction signal being attacked. • Constructs a Lyapunov matrix based complete-type LKF. • Ensure that the state errors of UMVs converge asymptotically to zero and has the prescribed H ∞ performance index γ 0. [ABSTRACT FROM AUTHOR]

This paper addresses the control of a nonlinear system affected by deadzone effects, using a constrained actuator. The system itself incorporates a second-order oscillatory dynamic actuator, with an unknown nonlinear input-output relationship. The proposed algorithm not only accommodates the deadzone constraints on control inputs but also considers the actuator's saturation limits in control input calculations. It introduces a trajectory tracking mechanism that, instead of directly following the primary trajectory, adheres to an alternative trajectory capable of stable tracking, gradually converging to the main trajectory while accounting for operational constraints. In practical control systems, the actuator's input-output relationship is often nonlinear and unknown, requiring inversion for model-based control. This paper employs an offline-trained neural network trained on synthetic data to identify and approximate the actuator's behavior. To optimize the control system's performance and ensure stability during sudden error changes, the control input operates in two modes: position and velocity control. This dual-mode control allows for continuous switching between the two, facilitated by an innovative optimization technique based on the gradient descent method with a variable step size. Simulation results validate the effectiveness of the proposed algorithm in controlling systems constrained by hard limits and featuring nonlinear oscillatory actuators, providing a valuable contribution to the field of control systems. • Developing a dual mode non-predictive control algorithm based on discrete-time system equations. • Utilizing an offline-trained neural network for estimating the inverse of the nonlinear actuator relationship. • Stable desired trajectory mapping based on inherent and actuation constraints while maintaining asymptotic stability. • Substituting the trajectory within a fixed range outside of the constant deadzone and inside the variable saturation. • Optimized balancing position and velocity control modes through a weighted average of two control forces. [ABSTRACT FROM AUTHOR]

This paper proposes a novel controller design using adaptation based modified super twisting control to facilitate trajectory tracking and hovering maneuvers for the quadrotor. The controller gains of the existing modified super twisting control require bounds on the disturbance for trajectory tracking and hovering of the quadrotor. In this paper, the controller gains are adapted using the proposed dynamic adaptation law without knowing the actual disturbance or their upper bounds. The controller is designed within a nonlinear framework without performing linearization of quadrotor dynamics, which enables the proposed controller to remain effective even when the states deviate significantly from their nominal values. The performance of trajectory tracking and hovering of the quadrotor in the presence of disturbance is demonstrated using numerical simulations. In order to assess the effectiveness of the controller, the performance of the adaptation based modified super twisting control is compared to the existing modified super twisting control, and the proposed controller outperforms the existing one. • Adaptive modified super twisting control for robust trajectory tracking and hovering. • Dynamic adaptation law relaxes the need for an upper bound on disturbance. • Numerical simulations show the validity of the proposed approach. • Performance is compared with the existing modified super twisting control. • Efficacy is demonstrated with Dryden wind and sinusoidal disturbances. [ABSTRACT FROM AUTHOR]

This paper addresses the secure quasi-synchronization issue of heterogeneous complex networks (HCNs) under aperiodic denial-of-service (DoS) attacks with dynamic event-triggered impulsive scheme (ETIS). The heterogeneity of networks and the aperiodic DoS attacks, which hinder communication channels and synchronization goals, present challenges to the analysis of secure quasi-synchronization. The ETIS leverages impulsive control and dynamic event-triggered scheme (ETS) to handle the network heterogeneity and the DoS attacks. We give specific bounds on the attack duration and frequency that the network can endure, and obtain synchronization criteria that relate to event parameters, attack duration, attack frequency, and impulsive gain by the variation of parameter formula and recursive methods. Moreover, we prove that the dynamic ETS significantly reduces the controller updates, saves energy without sacrificing the system decay rate, and prevents the Zeno phenomenon. Finally, we validate our control scheme with a numerical example. • A novel dynamic event-triggered impulsive scheme that involves forced event sequence is developed. • We provide specific bounds on DoS attack duration and frequency that the network can tolerate. • This paper handles the secure quasi-synchronization of heterogeneous complex networks under DoS attacks. • An exact expression for the synchronization error bound and the system decay rate is provided. • Zeno behavior is avoided. [ABSTRACT FROM AUTHOR]

This paper studies the leader–follower consensus problem for nonlinear multi-agent systems with actuator saturation by adaptive event-triggered scheme. This adaptive mechanism allows parameters in triggered mechanism to change automatically compared to fixed ones in traditional strategies. The paper introduces two distinct approaches to address the issue of saturation. The first approach is sector bounded condition, while the second relies on convex hull representation. Compared with the former one, the latter one can reduce the conservatism of controller design effectively. Furthermore, it is assumed that the nonlinear function adheres to incremental quadratic limitations, thereby offering a more comprehensive depiction of its nonlinear properties. Finally, the validity of the proposed approaches is demonstrated by one numerical example in the background of mechanical systems. • The leader–follower consensus problem for nonlinear multi-agent systems with actuator saturation is studied. • Adaptive event-triggered mechanism for multi-agent systems is considered. • Two methods for dealing with saturation are proposed, one is sector bounded condition and another is convex hull representation. • The nonlinear function is supposed to satisfy incremental quadratic constraints. [ABSTRACT FROM AUTHOR]

Motor bearing fault diagnosis is essential to guarantee production efficiency and avoid catastrophic accidents. Deep learning-based methods have been developed and widely used for fault diagnosis, and these methods have proven to be very effective in accurately diagnosing bearing faults. In this paper, study the application of generative adversarial networks (GANs) in motor bearing fault diagnosis to address the practical issue of insufficient fault data in industrial testing. Focus on the auxiliary classifier generative adversarial network (ACGAN), and the data expansion is carried out for small datasets. This paper present a novel transformer network and auxiliary classifier generative adversarial network (TRA-ACGAN) for motor bearing fault diagnosis, where the TRA-ACGAN combines an ACGAN with a transformer network to avoid the traditional iterative and convolutional structures. The attention mechanism is fully utilized to extract more effective features, and the dual-task coupling problem encountered in classical ACGANs is avoided. Experimental results with the CWRU dataset and the PU dataset in the field of motor bearing fault diagnosis demonstrate the suitability and superiority of the TRA-ACGAN. • A novel motor bearing fault diagnosis network named TRA-ACGAN is proposed in this paper. The TRA-ACGAN is based on transformer network and ACGAN and the combination of these two methods is novel. The TRA-ACGAN has the following advantages compared to existing methods: • The TRA-ACGAN completely abandons the tedious recurrent structure and conventional convolution layer, which can avoid the complex calculation process of classical models and reduces the complexity of the model. • Two additional tokens are added to the Transformer encoder of TRA-ACGAN's Discriminator to carry the label information of fault diagnosis and the source information of discrimination respectively. In this way, the identification process of the system is simplified. • By improving the structure of the discriminator, the TRA-ACGAN can avoid the dual-task coupling problem of recognition and diagnosis in the classical ACGAN and improve the accuracy of the system. • The TRA-ACGAN proposed in this article can achieve more effective training and diagnostic results than classical models. The diagnostic accuracy of TRA-ACGAN in the field of motor bearing fault diagnosis reached more than 99% in our experiments after training. [ABSTRACT FROM AUTHOR]

This paper tries to study the problem of finite-time synchronization for delayed semi-Markov reaction–diffusion systems. Based on the spatial and parametric characteristics of the considered systems, a new asynchronous boundary control scheme is proposed to ensure the finite-time synchronization of the drive and response systems. In the asynchronous boundary control scheme, only an actuator should be placed at the spatial boundary, which is more easier to implement and economical than the other non-boundary control strategies. Besides, the system parameters and controller follow two asynchronous semi-Markov chains for jumping, which is more practical than obeying one semi-Markov chain. Moreover, for the considered systems, we proposes a new lemma of finite-time stability, and by employing the inequality methods and variable substitution, we derive the criterion of finite-time synchronization and a correlative corollary. Finally, a numerical example and an application example on secure communication are carried out to support the developed approach. • Our study enriches the related research of semi-Markov RDSs. The obtained results guarantee the finite-time synchronization of semi-Markov RDSs, which is more effective and practical than asymptotic synchronization. • This paper proposes a novel asynchronous boundary control scheme, for the synchronization of semi-Markov RDSs. Compared with existing ones, our control scheme has obvious advantages in realizability and economy. • Based on the existing papers, in the designed controller, a constant is introduced, such that the intense chatter of controller could be reduced. • For the considered systems and designed controller, a new finite-time stability lemma is proposed, which enables the derivation of a finite-time synchronization criterion and a related corollary of this paper. [ABSTRACT FROM AUTHOR]

In recent years, distributed optimization problem have a wide range of applications in various fields. This paper considers the prescribed-time distributed optimization problem with/without constraints. Firstly, we assume the state of each agent is constrained, and the prescribed-time distributed optimization algorithm with constraints is designed on the basis of gradient projection algorithm and consensus algorithm. Secondly, the constrained distributed optimization problem is transformed into the unconstrained distributed optimization problem, and according to the gradient descent algorithm and consensus algorithm, we also propose the prescribed-time distributed optimization algorithm without constraints. By designing the appropriate objective functions, we prove the multi-agent system can converge to the optimal solution within any prescribed-time, and the convergence time is fully independent of the initial conditions and system parameters. Finally, three simulation examples are provided to verify the validity of the designed algorithms. • For distributed optimization problem with convex constraints, we design an effective prescribed-time distributed algorithm which can guarantee the multi-agent system converge to the optimal solution. • The designed algorithm in this paper can achieve convergence within any prescribed-time, and the convergence time is fully independent of the initial conditions and system parameters. • In this paper, we not only consider the condition that the agents' states are constrained, but also ensure that the multi-agent system can reach the optimal solution within any prescribed-time, which is more practical. [ABSTRACT FROM AUTHOR]

In this paper, the secure containment control issue for heterogeneous multi-agent systems subject to hybrid attacks is studied. In the network channels among agents, adversaries launch replay attacks such that the followers can only retrieve the previous information of their neighbors. To characterize the effects of replay attacks, a distributed auxiliary system with heterogeneous time-varying delay is constructed to estimate the convex hull of the leaders' state. The control input of each follower may be contaminated by the malicious false data injection (FDI) attacks with unknown bounded time-varying signals, which are intended to compromise the containment performance of the whole system. A local adaptive compensator is designed for each follower to eliminate the adverse effects of the FDI attacks. Based on the distributed auxiliary system and the local adaptive compensator, a secure distributed control protocol is proposed to ensure that the output trajectories of each follower can converge to the dynamic convex hull spanned by the leader outputs. Finally, two illustrative examples are provided to show the feasibility of the proposed secure control scheme. • This paper introduces a secure control protocol to dispose a more complicated attack situation. • The original Laplacian matrix is divided into several sub-Laplacian matrices associated with the heterogeneous time-varying delays. • The designed adaptive compensator can fully eliminate the adverse effects of the FDI attacks without using any information of the FDI attacks. [ABSTRACT FROM AUTHOR]

This paper addresses the automatic berthing of a maritime autonomous surface ship operating in a confined water environment subject to static obstacles, dynamic obstacles, thruster constraints, and space constraints due to shorelines. A safety-certified active disturbance rejection control (ADRC) method is proposed for achieving the automatic berthing task of an MASS in the presence of model uncertainties and ocean disturbances. An extended state observer (ESO) based on a second-order robust exact differentiator (RED) is employed to estimate an extended state vector consisting of internal model uncertainties and external ocean disturbances. With the aid of the RED-based ESO, a nominal ADRC law is designed to achieve the position and heading stabilization. To avoid collisions with static obstacles, dynamic obstacles, and shorelines, input-to-state safe high-order control barrier functions are used to guarantee safety. Optimized control signals are obtained based on a constrained quadratic programming (QP) problem within safety constraints. In order to translate the control signals into the individual thruster command, a constrained QP problem is further used to search for optimized commands in real time. It is proven that the closed-loop automatic berthing system is input-to-state stable. By using the proposed method, the MASS is able to reach the desired position and heading with collision avoidance. Simulation results verify the effectiveness of the proposed safety-certified ADRC method for automatic berthing. • This paper proposes an active disturbance rejection control method to achieve automatic berthing of an MASS. • The ISSf-HOCBFs are used for the achievement of collision avoidance with static/dynamic obstacles and shorelines. • A constrained QP problem is used to obtain the optimal commands in real time. [ABSTRACT FROM AUTHOR]

Performance requirements necessitate control designs that assure not only transient response specifications but also steady-state accuracy. Monotonic convergence of the tracking error is crucial for an efficient control design to prevent the performance degradation caused by overshooting. This needs a balanced consideration of both reaching conditions and the monotonic convergence, in the context of sliding mode control. In this paper, the dynamic behaviour of the dead-beat sliding mode control is characterized and the signum function is replaced by employing a non-switching one, in order to reduce chattering. The paper conducts a thorough analysis of monotonic convergence of both the switching and the non-switching error dynamics. By deriving the conditions for monotonic convergence, the control parameters can be strategically chosen to ensure monotonic convergence of the tracking error. Numerical and experimental results are presented to validate effectiveness of the proposed control scheme, which evaluate the tracking performance achieved by both the switching and the non-switching control methods. • The DBSMC approach uses a dead-beat reaching law for the global sliding-mode. • Both the switching and non-switching attractive laws are proposed. • The monotonic convergence is characterized by the MD-region and the SS-band. • The calculation of convergence steps of the DBSMC schemes is provided. [ABSTRACT FROM AUTHOR]

A tracking control scheme is proposed for complex dynamic network (CDN), where the CDN is regarded wholly as a dynamic composite system which consists of two mutually coupled subsystems. One subsystem consists of all nodes and the other consists of all links, and consider the weights of the links to be state variables in the latter subsystem. There are two parts in the structure of the proposed tracking control scheme: the designed controller of nodes and the synthesis of the coupling term in links. These two parts can guarantee two subsystems to asymptotically track the given reference targets, respectively. This approach originates from the communication transmission network (CTN). In CTN, for the network optimization management, the reference network topology (NT), such as the star topology, is required as a target for tracking in communication transmission (links) when each node in network needs to track its own target. The control scheme provided of this paper coincides with above requirement. And finally, a comparative simulation example is given for illustrating the effectiveness of the provided control scheme. • The dynamic equations of nodes and NT are represented by the matrix differential equations, this can contain many models in other papers and simplify the control scheme, which is rarely discussed. • The tracking targets of nodes and NT given in this paper are independent of each other, which is novel. • When all nodes track the reference targets, the eventual network layout is shown by the desired NT, which is novel. [ABSTRACT FROM AUTHOR]

To solve the position control problem of the induction motor with parameter perturbations, load disturbances, and modeling errors, a predefined-time position tracking optimization control method with prescribed performance is proposed. In practice, the rotor flux linkage of the induction motor can't be measured, and a predefined-time sliding mode observer (PTSMO) is applied to accurately estimate it. Additionally, predefined-time disturbance observers (PTDOs) are employed to identify the uncertainties in the motor system. The position and flux linkage controllers are then designed by integrating the predefined-time control approach with the prescribed performance function method, realizing accurate tracking control of the induction motor within a predefined time. Next, the adaptive genetic algorithm (AGA) and the improved particle swarm optimization (IPSO) technique are combined to optimize the designed controllers, enhancing the convergence rate and steady-state accuracy of the induction motor. Finally, comparative analyses through simulations and dSPACE simulated experiments validate the efficacy of the proposed control method, highlighting its applicability in practical motor systems. ● The predefined-time sliding mode observer is crafted to observe the rotor flux linkage, and the predefined-time disturbance observers are designed to estimate the system's uncertainties. This not only addresses the challenges of unmeasurable rotor flux linkage in practice, and the observation errors converge within a predefined time. ● This paper combines the predefined-time control and the prescribed performance method, ensuring both the transient and steady-state performances of the system, while also ensuring the motor states converge within a predefined time. ● This paper combines the AGA and the IPSO to generate the AGA_IPSO hybrid algorithm to optimize the primary controller parameters, which not only simplifies the tuning process of system parameters, but also enhances the system's convergence speed and steady-state accuracy.. [ABSTRACT FROM AUTHOR]

This paper mainly studies the consensus control strategy for a novel heuristic nonlinear multi-agent system. Compared with most existing related researches, firstly, the novel heuristic nonlinear multi-agent system has the ability to construct its communication network topology heuristically, and can withstand long-term DOS(Denial of Service) attacks, with the advantages of high practicality and security. Secondly, in order to control the multi-agent system, a control protocol based on both saturation effect and impulse control mechanism is studied, which has the advantages of high efficiency, low cost and wide applicability. Thirdly, for the multi-agent system, its dynamic model is constructed and analyzed by Lyapunov stability theory and matrix measure theory, and some sufficient conditions for achieving consensus are obtained. Finally, through two simulation experiments and some corresponding comparative analysis, the correctness, efficiency, and superiority of the theories proposed in this paper were verified. • A "heuristic communication network topology generation rule" is designed. It can scientifically solve the problem of the lack of specific design rules for communication connection among different individuals. • A "saturation impulse control protocol" is designed. It has advantages such as high efficiency and low cost. • This paper constructs a novel nonlinear dynamic model for the multi-agent system, which can withstand long-term DOS attacks. [ABSTRACT FROM AUTHOR]

To address the trajectory tracking issue of upper-limb rehabilitation exoskeleton with uncertainties and external disturbances, this paper proposes a fractional-order ultra-local model -based model-free finite-time robust controller (FO-FTRC) using predefined performance sliding surface. Different from previous model-free control strategies, a novel multi-input multi-output (MIMO) fractional-order ultra-local model which is a virtual model is proposed to approximate the complex uncertain nonlinear exoskeleton dynamics in a short sliding time window. This allows the design of controller to be independent of any exoskeleton model information and reduces the difficulty of controller design. The developed robust model-free control method incorporates a fractional-order quasi-time delay estimator (FO-QTDE), unknown disturbance estimator (UDE) as well as prescribed performance sliding mode control (PPSMC). The FO-QTDE is utilized to estimate the unknown lumped uncertainties which employs short time delayed knowledge only about the control input. However, the low-pass filter is always added for FO-QTDE when disturbances change fast, which leads to unavoidable estimation error. Then, UDE is designed to further eliminate the estimation error of FO-QTDE to enhance control performance. The PPSMC is constructed to converge sliding surface to zero in a finite time. Besides, the sliding surface is always limited in performance boundaries. After that, the overall system stability and convergence analyses are demonstrated by using the Lyapunov theorem. Finally, with the comparison to other methods of α -variable adaptive model free control (α -AMFC), time-delay estimation-based continuous nonsingular fast terminal sliding mode controller (TDE-CNFTSMC), time delay estimation (TDE)-based model-free fractional-order nonsingular fast terminal sliding mode control (MFF-TSM) and fractional-order proportion–differential (PD β), the co-simulation results on 7-degree-of-freedom (DOF) iReHave upper-limb exoskeleton virtual prototype and experiment results on 2-DOF upper-limb exoskeleton are obtained to illustrate the effectiveness and superiority of the proposed FO-FTRC method. • A fractional-order ULM is proposed to approximate complex exoskeleton to achieve MFC. • A QTDE and disturbance estimator is designed to eliminate the lumped uncertainties. • The paper achieves the FT stabilization under fractional-order ULM design framework. • The co-simulation experiments on 7-DOF iReHave exoskeleton and experiment on 2-DOF. [ABSTRACT FROM AUTHOR]

In a Networked Control System (NCS), event-triggering is often used to reduce the number of network transmission instances and improve network usage. This paper considers transmitting output data rather than the estimated state for such event-triggered schemes. Output transmission facilitates efficient transmission by reducing the size of the packets sent over the network. However, since the state observer utilizes delayed information output transmission deteriorates system performance and requires improved observer-based prediction schemes for closed-loop control. The performance of a new scheme involving the transmission of sequential output information in a single packet to improve the state observer's performance is demonstrated in this paper. A sequential observer is constructed that uses successive output information to better observe the states, and a prediction scheme is used to take care of the delays due to event-triggering, network delays, and dropouts. Further, event-triggering is employed in both the feedback and forward channels. The demonstrated efficacy of this sequential approach in the networked control of an inverted pendulum system and a DC motor system emphasizes its potential as a practical solution for improved control in NCSs, particularly in the face of network constraints and communication challenges. • The work deals networked control systems with the resource constrained communication network by incorporating event-triggering in both the feedback and forward paths. • Using a predictive control method, the network delays and dropouts, and the dropouts due to event-triggering are dealt with. • A new sequential observer is constructed to process sequential output information at the remote end for improving the state estimates. • The proposed observer reduces the communication load considerably while effectively exploiting the packet size in the network. [ABSTRACT FROM AUTHOR]

Machine learning performs well in many problems. However, the tendency to generate predictions that violate theoretical knowledge makes it difficult to apply to practical processing. To resolve this situation, this paper combines domain knowledge with a data-driven model, proposes a theory-guided machine learning framework based on a parameter transfer strategy, and applies it to the width prediction of plates after multiple passes of hot rolling. The framework applies a swarm optimization algorithm to the original theoretical model and generates numerous highly-physical consistent samples. The established deep neural network (DNN) model is trained with simulated data, and the parameters are fine-tuned using a parameter transfer strategy combined with actual data to ensure excellent adaptation to the actual environment based on adequate learning of theoretical knowledge. In tests, the proposed model had the best overall prediction performance in this paper. Meanwhile, the developed model is consistent with the existing perception of rolling theory. This allows for the quick and reliable application of machine learning models in production. [Display omitted] • A novel theory-guided machine learning framework for width prediction of rolled plates. • Pre-training of the model using the data generated by the optimized theoretical model. • The proposed model has the best accuracy in both sufficient and less actual samples. • The variable influence of the proposed model is consistent with theoretical perceptions. [ABSTRACT FROM AUTHOR]

This paper proposes a fractional-order time-varying sliding mode control method with predefined-time convergence for a class of arbitrary-order nonlinear control systems with compound disturbances. The method has global robustness and strongly predefined-time stability. All state errors of the system can converge to zero at a desired time, which can be set arbitrarily with a simple parameter. The strongly predefined-time convergence of the system is clearly demonstrated by the analytic expression of state error, which is obtained by solving fractional-order differential equations corresponding to the sliding mode function. The simulation results show that the proposed method still has good control performance in the presence of input saturation and external interference. • The main contributions of this paper are summarized as follows: • A predefined-time fractional-order time-varying sliding mode control scheme is proposed for a class of arbitrary order nonlinear control systems with complex disturbances. All state errors are simultaneously converged to zero by the scheme at a time, which can be presented by a simple parameter. • Different from most of the existing methods, this paper obtains the analytical expression of the state errors by solving the fractional-order differential equation to prove the predefined-time convergence of the system. • The fractional-order term and time-varying term are introduced into the proposed control scheme, which improves the control performance and makes the system have global robustness. The simulation results show that the proposed controller has strong robustness and resistance to input saturation. [ABSTRACT FROM AUTHOR]

This paper deals with the appealing problem of fixed-time fault-tolerant attitude control, using attitude-information-only, for a flexible spacecraft under the influence of inertial parametric variations, external disturbances, and multiple actuator faults while suppressing the flexible appendages' vibrations without using additional sensors or smart vibration suppression actuators. First, an adaptive fixed-time model-free observer (AFTMFO) is designed, using the attitude information, for rapid estimation of unavailable angular velocity. A novel adaptive continuous control is then synthesized based on an anti-unwinding fast fixed-time nonsingular sliding surface (AFFTNSS), utilizing variable gains in both the control law and sliding surface; that simultaneously alleviates the chattering but also improves the convergence speed when compared to existing fixed-time approaches. The proposed scheme offers superior performance characteristics such as velocity sensor-free fixed-time attitude maneuvering with high pointing accuracy, fault tolerance, vibration suppression, nonsingular and chattering-free control. The spacecraft can carry out the coveted control objective in a predeterminable time independent of the knowledge of initial states while overcoming the unwinding effect to reduce the control effort and time. The fixed-time closed-loop stability of the proposed scheme is corroborated via Lyapunov techniques. Finally, a comparative simulation analysis with the existing results elucidated the proposed scheme's efficacy. • Fixed-time fault-tolerant attitude control is proposed for flexible spacecraft with only attitude information. • Model-free observer-based rapid estimation of quaternion derivative and furnishing of angular velocity is designed. • Novel fast nonsingular and anti-unwinding terminal sliding mode based adaptive fault-tolerant attitude control. • Fixed-time convergence of attitude trajectories is studied in the paper. [ABSTRACT FROM AUTHOR]

This paper mainly focuses on solving the global μ -synchronization issue of complex dynamical networks (CDNs) by a novel event-triggered impulsive control (ETIC) method with time delays. This method combines the advantages of impulsive control and event-triggered control and gets rid of the limitation that the Lyapunov function decreases strictly monotonically with the sequence of event triggers. An event-triggered mechanism is specifically designed to realize μ -synchronization for CDNs in this paper, which means that event-triggered control has been applied to μ -synchronization field for the first time. Compared with periodic impulsive control, ETIC only produces control effects when the event-triggered mechanism are met, which is more in line with the actual situation. By Lyapunov–Razumikhin, recursion, etc, some valid global μ -synchronization criteria of CDNs are obtained and also Zeno behavior is avoided. Additionally, coupled delays and uncertainties are considered in CDNs. Finally, two numerical examples are shown to demonstrate the correctness of the designed ETIC strategy. • A novel efficient event-triggered impulsive control method is proposed. • Parametric uncertainties and delays are considered in complex dynamical networks. • Event-triggered control has been applied to μ -synchronization for the first time. • Some less conservative μ -synchronization criteria are gained. • Zeno behavior is avoided. [ABSTRACT FROM AUTHOR]

This paper presents a control method for trajectory tracking of a robotic manipulator, subject to practical constraints and uncertainties. The proposed method is established upon an adaptive backstepping procedure incorporating a tangent-type barrier Lyapunov function and it preserves some important metrics of trajectory tracking such as fast and user-defined settling time response and robustness against actuation faults and unknown control gain. The proposed design maintains the system trajectory within a prescribed performance bound and relaxes the assumption of the bounded initial condition. These salient features preserve the system within a safety bound and, consequently, guarantee the system stability and safety. The performance of the proposed control method is validated on a 3-DOF PUMA 560 robotic manipulator benchmark model, with different operation scenarios. The simulation results confirm the effectiveness and robustness of the proposed control method. • This paper presents a prescribed trajectory tracking control of a robotic manipulator, subject to practical constraints. • The proposed design preserves user-defined settling time and unknown control gain. • The method maintains the system trajectory within a prescribed performance bound and relaxes the bounded initial condition. • A computationally efficient core function with an unknown scalar gain is used to enable real-time implementation in practice. • The performance of the method is validated on a 3-DOF PUMA 560 robotic manipulator benchmark model. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of event-triggered mechanism(ETM)-based sliding-mode fault-tolerant control (FTC) for a six-rotor Unmanned Aerial Vehicle (UAV) with dead zone input (DZI) cases, considering potential actuator and sensor faults. Initially, a dynamic ETM is designed, followed by the development of a non-fragile observer utilizing this designed ETM. An integral sliding surface (SS) is then designed in the observation space, and the system is augmented and treated as a variable time delay system. Subsequently, sufficient conditions to ensure the stability of the augmented system with an H ∞ performance index γ are obtained using the Lyapunov-Krasovskii function. Next, a sliding mode control (SMC) law is formulated to guide the sliding variables to the SS in finite time. Furthermore, sufficient conditions for ensuring system stability with an H ∞ performance index γ are decoupled, and the calculation methods for the non-fragile observer gain matrix and the sliding mode gain matrix are obtained. Finally, to validate the effectiveness of the proposed method in this paper, simulation experiments are conducted. • Considering the existence of dead zone input problem in the control system of a six-rotor Unmanned Aerial Vehicle, a sliding mode fault-tolerant control law that can compensate for dead zone input is designed based on a non-fragile observer, and a sliding mode fault-tolerant control framework for a six-rotor Unmanned Aerial Vehicle based on event-triggered mechanism is established. • Considering the variable time-lag state observation system obtained by combining the dynamic event-triggered mechanism proposed in this work, a new integral-type sliding surface is designed in the state observation space. Compared with the conventional sliding mode control method, the method proposed in this paper not only has smaller system chattering but also enables the sliding variables to reach the steady state in a shorter time. • In order to reduce the computational burden of solving the to-be-designed matrix during the design process, unlike the construction and design methods of linear matrix inequality in the existing methods, this paper constructs an linear matrix inequality with a smaller computational burden to solve the to-be-designed content, and eliminates the equation constraints in the sufficient conditions for the stability of the system by utilizing the matrix equations, which yields a better fault-tolerant control effect. [ABSTRACT FROM AUTHOR]

This paper proposes a reinforcement learning-based formation-surrounding control method for multiple quadrotor unmanned aerial vehicles (UAVs) pursuit-evasion (MPE) games system subject to external disturbances. In the framework of the MPE games, the pursuers aim to equally surround the evaders which try to avoid being surrounded when forming the desired formation. By constructing position and attitude tracking error subsystems of quadrotor UAV, this paper proposes two control strategies which combines the feedforward control technique and reinforcement learning (RL) method. First, two novel cost functions are presented for the quadrotor UAV with external disturbances. Then, two control schemes based on RL have been developed to guarantee the stability of the tracking error subsystem. Subsequently, two critic-only neural networks (NN) weight update laws that only satisfy finite excitation conditions are proposed to estimate the optimal cost function. Furthermore, Nash equilibrium for multiple quadrotor UAVs is achieved by means of RL strategy to solve the Hamilton-Jacobi-Isaacs (HJI) equations. And the property of equally surrounding is proved for the first time by utilizing Euler's formula in this paper. Finally, the numerical simulation results are given to show the effectiveness and superior performance of the proposed control method. [ABSTRACT FROM AUTHOR]

This paper studies the quantized iterative learning control with encoding–decoding mechanism of a class of impulsive differential inclusion systems with random data dropouts. First, the set-valued mappings in the differential inclusion systems are transformed into single-valued mappings by using the Steiner-type selector. Then, a learning algorithm based on the intermittent update principle is designed to address the data asynchronism problem caused by two-sided data dropouts. If the data are successfully transmitted at the actuator and measurement sides, then the control input is effectively updated. Furthermore, a suitable scaling sequence is introduced to ensure the system output to achieve zero-error tracking performance for a desired trajectory. An upper bound of the quantization level is determined such that the quantization error is always bounded. The results show that the quantization method reduces the burden of network communication at the cost of increasing the amount of computation, and the learning algorithm does not require the data dropouts to satisfy a certain probability distribution. Finally, the effectiveness of the learning algorithm is verified by numerical simulations of the switched reluctance motor system. • This paper enriches the iterative learning control (ILC) results for uncertain systems. • We establish an ILC research framework for continuous-time systems with unreliable networks. • We construct a novel learning algorithm to deal with the data asynchronous problem. [ABSTRACT FROM AUTHOR]

This paper deals with a comparative evaluation of nonlinear controllers based on the linear regression technique, which is a machine learning algorithm for maximum power point tracking. In the past decade, most photovoltaic systems have been equipped with classical algorithms such as perturb and observe, hill climbing, and incremental conductance. The simplicity of these techniques and their ease of implementation were seen as the main reasons for their utilization in photovoltaic systems. However, researchers' attention has recently been attracted by artificial intelligence-based techniques such as linear regression, which offer better performance within the bounds of the nonlinearity of photovoltaic system characteristics. An adaptive terminal synergetic backstepping controller is developed in this paper for a single-ended primary inductance converter. This control scheme is based on the combination of a non-singular terminal synergetic technique with an integral backstepping technique and equally a neural network for the approximation of unmeasured or inaccessible variables that guarantees the finite-time convergence. The proposed controller was further verified under virtual and real environmental conditions, and the numerical results obtained from Matlab/Simulink software under various test conditions, including load variations, show that the adaptive terminal synergetic backstepping controller gives satisfactory performance compared to the adaptive integral backstepping controller used in the same climatic conditions. • New controller designed by combining adaptive terminal synergetic and integral backstepping techniques. • Reference voltage generation using machine learning regression algorithm. • Estimation of unmeasured variables through Neural Network. • Improved particle swarm optimization method to determine the design parameters. • Comparison of proposed controller with others under real climatic conditions. [ABSTRACT FROM AUTHOR]

The research investigates the fixed-time command-filtered composite adaptive neural fault-tolerant (FCCANF) control issue of strict-feedback nonlinear systems (SFNSs). There exist unknown functions and bounded disturbances in the considered systems. Radial basis function neural networks (RBFNNs) will be used in the estimate of the unknown functions. By the serial–parallel estimation models (SPEMs), the forecast biases and the track biases can change the weights of RBFNNs and the approximate characteristics of RBFNNs will be improved. Then, utilizing the novel fixed-time command filter and adaptive disturbance observers, the issue of complex explosion will be effectively solved and the external disturbance is effectively compensated. Subsequently, by utilizing the adaptive control technique, a novel FCCANF controller is developed. Additionally, we have that the system internal variables are bounded and the output variable inclines to a little interval around zero in fixed time which is not determined by the system initial variables. Eventually, numerical and practical examples are shown to prove the availability of the obtained control technique. • For the paper [23], a second-order fixed-time command filter with the symbolic function is introduced to estimate the derivatives of the virtual controllers. However, in this manuscript, a first-order fixed-time command filter with the smooth hyperbolic tangent function is designed to estimate the derivatives of the virtual controllers. Then, the compensation of filtering errors is not considered in the paper [23]. However, in this manuscript, the compensation of filtering errors is considered. • To improve the RBFNN approximation, the novel fixed-time convergence SPEMs are created and it is the first time to use the matrix element technique to solve the fixed-time patchwork problems for the adaptive laws. Then, to estimate the lumped disturbance, the fixed-time convergence adaptive neural disturbance observers are utilized firstly. • Compared with the papers [22] and [24], based on [25], the hyperbolic tangent functions are introduced to solve the singular problem. [ABSTRACT FROM AUTHOR]

In this paper, an innovative event-triggered optimal tracking control algorithm is proposed for input saturated strict-feedback nonlinear systems with unknown dynamics. In order to reduce the requirement of configuring a complete suit of sensors and enhance the reliability of the controlled system, a neural networks (NNs) based adaptive state observer is developed firstly to reconstruct the system states. Subsequently, based on the state estimation information, a hybrid-triggered feedforward controller is designed to transform the original tracking control problem into an equivalent regulation issue, which is then solved by developing an event-triggered optimal controller. Therefore, the final controller consists of a hybrid-triggered feedforward controller and an event-triggered optimal controller. In order to make the actual input signals of the two controllers be updated simultaneously, a synchronization-oriented triggering rule is established by using multiple triggering errors. By virtue of this unique framework, the proposed control scheme can not only minimize the predefined cost function, but also greatly reduce the data transmission. What is more, the convergence properties of the proposed control strategy are achieved by using Lyapunov theory. It is important to note that unlike the widely adopted observer-controller framework, where the separation principle holds for the design of the state observer, there is a considerable coupling relationship between the error dynamics of the state observer and the event-triggered optimal controller in this paper. The distinguishing feature of the proposed method is its ability to ensure a satisfactory level of precision in both state estimation and tracking control, even in the presence of control saturation issues. At last, the proposed control strategy is applied to the tracking control problem of a high-order robot system and marine surface vehicle to demonstrate its effectiveness. • A novel event-triggered optimal tracking control framework is proposed. • A phenomenon we defined as the "jump of error surfaces" is noticed and tackled via filter technique. • A multi-error based adaptive triggering rule is established to improve the control performance. • The proposed method not only achieves the optimality but also lowers data transmission. [ABSTRACT FROM AUTHOR]

This paper proposes a novel robust tracking control scheme for discrete time linear uncertain Multiple-Input Multiple-Output (MIMO) systems subject to time-varying delay on the states. The considered system is affected by unknown but norm bounded uncertainties on parameters as well as matched disturbances on the states. The designed controller is based upon a proposed novel integral sliding surface and a new switching type of reaching law. Sufficient conditions based on Linear Matrix Inequalities (LMIs) and a suitable Lyapunov–Krasovskii Functional (LKF) are derived in order to guarantee the asymptotic stability of such system. The proposed controller ensures a good tracking performance despite the presence of the time varying delay and the matched/unmatched disturbances. Moreover and thanks to the proposed integral surface, the time reaching phase is eliminated and the chattering phenomenon is significantly reduced. The proposed controller is applied on an Autonomous Underwater Vehicle (AUV) to follow a prescribed desired trajectory. The simulation results illustrate the effectiveness of such controller. • The problem of the design of a robust discrete time integral sliding mode controllers for the tracking of an uncertain MIMO systems subject to time-varying delay on the states, and under the presence of matched and unmatched uncertainties has never been investigated before anyway. • Robustness against uncertainties is tackled in the present paper by considering the case of matched disturbances and unmatched Unknown But Norm Bounded (UBB) uncertainties. • In the current study we propose a novel discrete switching type reaching law, which is an extension of the one presented in Bartoszewicz and Latosinski (2014) to the MIMO case in the presence of delays, matched disturbances and unmatched uncertainties as well. • Based on a Lyapunov–Krasovskii Functional, the present work provides a sufficient condition guaranteeing the asymptotic stability of the closed-loop systems. With regard to that condition a new Integral Sliding Surface (ISS) has been proposed, which by canceling the reaching phase, it improves the performances of the designed controllers. • Discrete sliding mode controllers have been designed to ensure the occurrence of the sliding mode in finite time. One of these controllers lessens the chattering phenomenon. • The effectiveness of the proposed control algorithms are tested on an Autonomous Underwater Vehicle (AUV). The simulation results are promising. [ABSTRACT FROM AUTHOR]

This paper investigates the adaptive guaranteed cost stabilization (AGCS) problems for two classes of high-order nonlinear systems with unknown parameters (vector) and time delays. Firstly, based on the high-order fully actuated (HOFA) system approaches, the Lyapunov–Krasovskii functional (LKF) and the guaranteed cost control (GCC), a new AGCS strategy is proposed for HOFA nonlinear system with unknown parameter vector and time delays. Then, based on the above result, another AGCS controller for a class of strict-feedback systems (SFSs) with unknown parameters and time delays is obtained. Two designed controllers ensure that all of the states of two closed-loop systems are global boundedness, and preset arbitrarily the upper bound of cost functions (UBCFs) characterizing the output performance. More importantly, the UBCFs are independent of system initial values, unknown parameters (vector), and even time delays, which is difficult to achieve by using existing control methods. To do this, this paper introduces a local smooth nonlinear function (LSNF), and gives its corresponding lemma, which provide an important mathematical tool. Finally, three simulation examples, including an application in the electromechanical system, are given to prove the effectiveness and the practicability of our proposed control method. [ABSTRACT FROM AUTHOR]

Induction motors (IMs) are extensively used in industrial sector. This kind of machine is subjected to several stresses that could interrupt their normal operation. An excessive stress can generate some symptoms before the IM fall in failure situation. Therefore, incipient detection of these symptoms permits the shutdown of IMs in order to avoid total destruction. Fault detection is then the main objective of diagnosis systems. Stator inter-turn short-circuits (SITSC) constitutes an important amount of cause of IM breakdown. However; unbalance supply voltage (USV) is one of the advantageous factors that affect IMs operation. Thus, in order to avoid false alarm induced by USV, the diagnosis system must make difference between USV and SITSC faults. This paper presents an efficient approach to estimate SITSC percentage and detect USV occurrence using Artificial Intelligent (AI) tool. Artificial neuronal network (ANN) plays the key-role of the proposed diagnosis system. A fault Classifier of SITSC and USV is carried out using multi-layer perceptron neuronal network (MLP-NN). The training, testing and validation phases of MLP-NN need the dataset creation. The required data is obtained from both simulated mathematical model of IM and laboratory test-bed. The reached results show the sensitivity and the well-functioning of the proposed diagnosis system. • The detection of Stator Inter-Turn Short-Circuits fault is much more based on its severity and location. • The presence of Unbalanced Supply Voltage makes us unable to know whether there is a SITSC fault or not; that's why identification of Unbalanced Supply Voltage is also very important. • The challenge of this work is to estimate the percentage of Stator Inter-Turn Short-Circuits and detect the presence Unbalanced Supply Voltage utilizing only current & voltage signals. • An MLP-NN is created and simulated for a mathematical model of the motor; in order to choose the appropriate architecture and to verify the efficacy of the proposed diagnosis system. • In the experimental investigation, no additional sensors are required since the terminal voltage and current measurements are sufficient. • the experimental investigation is adopted in order to prove the effectiveness of MLP-NN by using untreated cases of Stator Inter-Turn Short-Circuits fault and Unbalanced Supply Voltage. • The work in the paper has been compared to similar ones, and it has been found that the proposed method is efficient and has good accuracy compared to the others. • The work in the paper can be implemented on microcontroller due to its simplicity. [ABSTRACT FROM AUTHOR]

In this research paper, we investigate the problem of remote state estimation for nonlinear discrete systems. Specifically, we focus on scenarios where event-triggered sensor schedules are utilized and where packet drops occur between the sensor and the estimator. In the sensor scheduler, the SOD mechanism is proposed to decrease the amount of data transmitted from the sensor to a remote estimator and the phenomena of packet drops modeled with random variables obeying the Bernoulli distribution. As a consequence of packet drops, the assumption of Gaussianity no longer holds at the estimator side. By fully considering the non-linearity and non-Gaussianity of the dynamic system, this paper develops an event-trigger particle filter algorithm to relieve the communication burden and achieve an appropriate estimation accuracy. First, we derive an explicit expression for the likelihood function when an event trigger occurs and the possible occurrence of packet dropout is taken into consideration. Then, using a special form of sequential Monte–Carlo algorithm, the posterior distribution is approximated and the corresponding minimum mean-squared error is derived. By contrasting the error covariance matrix with the posterior Cramér–Rao lower bound, the estimator's performance is assessed. An illustrative numerical example shows the effectiveness of the proposed design. [Display omitted] • Limited bandwidth networked systems require sensors scheduling algorithm design. • Event-based particle filter is designed in conjunction with scheduling algorithms and packet dropout. • With Cramér–Rao lower bound the proposed scheduling algorithms improve clearly the filter performance. • Innovation scheduling allows robustness against packet dropouts. [ABSTRACT FROM AUTHOR]

This paper considers the aperiodic intermittent control (AIC) for linear time-varying systems (LTVSs), where the occurrence instants are determined by an event triggering mechanism based on Lyapunov functions. For LTVSs, most of the existing results are demanded that the feedback controls are exerted all the time. In fact, in many practical applications, the applied controls are unnecessary/impossible to be imposed all the time. Therefore, the event-triggered AIC is introduced in this paper for LTVSs, and the uniformly stability, globally asymptotic stability and finite-time stability are proposed for LTVSs with event-triggered AIC, respectively. In addition, by using the piecewise constant feedback control method, effective intermittent controllers are designed for LTVSs. Finally, we present two numerical examples to illustrate the efficacy of the derived results. • The AIC is introduced, and its occurrence instants are determined by event triggering mechanisms, which makes the designed control in this paper more flexible and can reduce the cost of control. By using these event-triggered AIC, the uniformly stability, globally asymptotic stability and finite-time stability are proposed for LTVSs, respectively. • The event-triggered aperiodic intermittent controllers are designed for LTVSs by using the piecewise constant feedback methods in this paper, which extends the feedback controller designed in Tan et al. (2016) to the event-triggered aperiodic intermittent controller for investigating the finite-time stability of time-varying systems. [ABSTRACT FROM AUTHOR]

This paper analyzes global robust exponential stability of interval bidirectional associative memory (BAM) neural networks with multiple time-varying delays, proposes a direct method based on system solutions, and gives sufficient conditions under which interval BAM neural networks have a unique and globally robustly exponentially stable equilibrium point. This method not only avoids the difficult to set up any Lyapunov–Krasovskii functional, but also derives simpler global robust exponential stability criteria. Compared with the data from other literature, the robust exponential stability criteria obtained in this paper have been presented to have more merits theoretically and numerically. • A new method based on system solutions is proposed to investigate robust GES criteria. • The obtained robust GES criteria are more effective than the existing ones. • The method is applicable to several neural network models after a small change. [ABSTRACT FROM AUTHOR]

This paper proposes a new adaptive super-twisting global integral terminal sliding mode control algorithm for the trajectory tracking of autonomous robotic manipulators with uncertain parameters, unknown disturbances, and actuator faults. Firstly, a novel global integral terminal sliding mode surface is designed to ensure that the tracking errors of autonomous robotic manipulators converge to zero in finite time and the global robustness of the system is also enhanced. Then a new adaptive method is devised to deal with the adverse effect of nonlinear uncertainty. To suppress the chattering phenomenon, the adaptive super-twisting algorithm is used in this paper, which can ensure that the control torque is a continuous input signal. Based on the adaptive mechanism, the adaptive super-twisting global integral terminal sliding mode controller is developed to provide superior control performance. The stability analysis of the system is demonstrated by using the Lyapunov method. Ultimately, the effectiveness of the control scheme is confirmed by a simulation study. • A novel adaptive finite-time global integral terminal sliding mode control scheme is designed. • A novel global integral terminal sliding mode surface is proposed to ensure faster convergence and better global robustness. • A novel adaptive law is designed to adjust the estimation parameters according to the change of system state to avoid overestimation. • The actuator fault is considered to make the proposed control logic more suitable for practical applications. [ABSTRACT FROM AUTHOR]

The three-phase four-wire voltage source inverter (3P4W VSI) is widely used in applications like uninterrupted power supply (UPS) and bidirectional onboard charger. The increasing power density demand requires higher switching frequency and lower switching loss. To fulfill the conflicting objectives, two-fold methodology is proposed in this paper: 1) SiC/Si hybrid switches (HyS) together with recently reported gate trigger are reported for the first time in the 3P4W VSI; 2) the natural point voltage is controlled to track a sinusoidal voltage with the frequency equal to 3 times of fundamental frequency in order to achieve higher DC-bus voltage utilization rate, and further reduce the switching loss. The traditional PI controller is very hard to achieve desired performance due to both the nonlinearity and the variant reference of the natural point voltage control system. Thereby, the super-twisting sliding mode control (ST-SMC) is proposed in this paper to achieve desired tracking performance and fast dynamic response. The effectiveness and superiority of the system are verified by both simulation and experiment comparison with the existing methods using a 5 kW prototype. • A new trigger circuit proposed recently is used for the first time in a 4 legs inverter. • The relationship of DC-bus voltage and HyS switching loss is derived and testified. • The super-twisting sliding mode control achieves high tracking accuracy in 3P4W inverter. • HyS and suitable controller comprehensively improve the performance of 3P4W inverter. [ABSTRACT FROM AUTHOR]

The motion of an autonomous ship is different from that of ground and aerial robots due to its maneuvering and environmental constraints. As a result, many techniques have been introduced for autonomous ship path planning. This paper presents a novel technique for global and local navigation planning of autonomous ships under complex static and dynamic constraints. Our technique, termed safety-enhanced path planning (SPP), has been developed to avoid potential collisions with underwater obstacles near seaside areas. SPP pre-processes the map to preserve the shape of visible obstacles and mark a safety-outline around the shores. Subsequently, an offset safety line (OSL) is drawn about the original shore to protect the ship when passing close to threat-defined offshore areas. The global path is produced with an enhanced A* multi-directional algorithm, considering the kinematic constraint of the ship. To ensure optimal path quality, the global path is further refined with a smoothing filter to improve consistency and smoothness. Additionally, local navigation is introduced to help the autonomous ship avoid collisions with other obstacle ships. Local offset trajectories are produced with 4th and 5th degree polynomials along longitudinal and lateral coordinates in time t. Distance closest point approach (DCPA) is utilized for early obstacle prediction to help the ship maneuver in complex dynamic obstacle avoidance scenarios. The trajectory set is filtered with an efficient cost policy to obtain the best trajectory for dynamic collision avoidance. We conduct simulations in MATLAB and compared with other maritime path planning methods to verify the effectiveness of our approach. • This paper presents a novel framework for global and local ship navigation. • Introduces a novel SPP for global path generation with shore extension. • Comparison results confirm SPP's efficacy, especially on complex maps. [ABSTRACT FROM AUTHOR]

This paper investigates the attack estimation and state reconstruction problem for linear cyber–physical systems with state delay and simultaneous sensor and actuator attacks. Reduced-order observer is designed for the augmented system to simultaneously estimate the actuator attack, sensor attack and the state of system. By adopting the double integral term into the Lyapunov–Krasovskii functional and decomposing the cross term, the delay-dependent results are obtained. The method proposed in this paper can accurately estimate the state, actuator and sensor attacks simultaneously without additional design. Compared with the previous method, the time delay information is fully utilized and the conservation is greatly reduced. Finally, the correctness of the results is verified by simulation. • Delay-dependent conditions are obtained for cyber–physical systems with state delay. • Both attack estimation and state reconstruction are considered. • Actuator attacks, sensor attacks and system state are estimated simultaneously. [ABSTRACT FROM AUTHOR]

This paper presents an improved cascaded model-free predictive speed and current control with the periodic and aperiodic disturbances suppression to achieve a smooth speed. The cascaded structure has an external speed loop and an internal current loop, both implemented with model-free predictive control to enhance the robustness of the controller. The current loop is designed based on the finite control set model-free predictive current control (FCS-MFPCC) strategy with an ultra-local model to regulate the stator currents, and the speed loop uses the proposed continuous control set model-free predictive speed control (CCS-MFPSC) to make full use of the excellent dynamic performance of the current-loop controller. To suppress the periodic disturbance that exists in the PMSM system, an improved parallel quasi-resonant controller (QRC) with an error limitation is embedded into the CCS-MFPSC, which can generate the compensated current. Based on the stability condition, the stability of the proposed MFPSC-QRC strategy is directly analyzed in the z-domain. Finally, the effectiveness and feasibility of the proposed cascaded model-free predictive speed and current strategy are validated on a PMSM test platform. • This paper proposes a cascaded strategy for PMSM drives by combining CCS-MFPSC and FCS-MFPCC. • The CCS-MFPSC is designed for the speed loop to enhance the dynamic performance • An improved QRC is studied to suppress the aperiodic and periodic disturbances simultaneously. • The stability is directly analyzed in the z-domain. [ABSTRACT FROM AUTHOR]

This paper studies the prescribed-time bipartite consensus problem for multiple Euler–Lagrange systems (MELSs) under directed matrix-weighted signed graph, in which input-to-output redundancy, external disturbances and uncertain dynamic terms have been taken into consideration. Firstly, this paper proposes the prescribed-time hierarchical control (PTHC) algorithm to tackle the aforesaid issue. It is worth pointing out that the convergence time can be arbitrarily prescribed based on actual engineering needs. Then, the corresponding sufficient conditions for achieving the prescribed-time bipartite consensus are obtained by invoking Lyapunov stability analysis. Eventually, the numerical simulation results are performed, which vividly reflect the effectiveness and feasibility of the developed control algorithm. • The prescribed-time control algorithm allows to predetermine the convergence time. • The MELSs with input-to-output redundancy can provide better flexibility. • A hierarchical sliding mode control algorithm solves the challenging problem. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the real power sharing and frequency regulation of distributed generators in islanded microgrids with abnormal asynchronous stochastic cyber attacks, which is of great significance to the information security and stable operation of microgrids. Firstly, considering the possible cyber attacks in the communication network, a distributed non-fragile controller with coupled memory delay is proposed according to the nonperiodic sampled-data control. Then, the construction of delay-dependent two-sided looped-functional makes the Lyapunov–Krasovskii functional contain more delay and sampling information and relaxes constraints on free matrices. In addition, based on the enhanced integral inequality technique and the linear convex combination method, a sampling-based consensus protocol is presented to solve the issues of real power sharing and frequency regulation in the islanded microgrids. Finally, to verify the effectiveness and feasibility of the designed control strategy, a modified IEEE 34-bus test system is used for the experiment. • This paper studies the real power sharing and frequency regulation issues in islanded microgrids and fully considers a class of stochastic cyber attacks which are abnormal and asynchronous with the system state. In addition, a new secondary control model considering such cyber attacks is established, which facilitates the study of the security performance of microgrids in complex and variable network environments. • Considering the possible disturbance of the controller in the physical platform and the delay of information transmission, a distributed non-fragile controller with coupled memory delay is designed to be as close to the real situation as possible. • A new delay-dependent two-sided looped-functional is introduced to make the Lyapunov–Krasovskii functional contain more delay and sampling information and reduce constraints on free matrices. Based on the linear convex combination and aperiodic sampled-data method, we can save more network resources and reduce the sampling data. [ABSTRACT FROM AUTHOR]