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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]

This paper investigates safety-critical event-triggered control (ETC) for nonlinear systems. It proposes a new ETC strategy that ensures event-triggered safety by using a tunable input-to-state safe barrier function. Then, a method is devised to effectively save communication resources and reduce computational load through the construction of a suitable dynamic event-triggered mechanism. Furthermore, the exclusion of Zeno behavior is guaranteed for the closed-loop system. Finally, this paper culminates by presenting two illustrative examples that showcase the effectiveness of proposed control strategies. • Address the issue of sampling errors and provide a less conservative approach. • Ensure event-triggered safety of nonlinear systems. • The definite exclusion of Zeno behavior in event-triggered control. [ABSTRACT FROM AUTHOR]

Discrete-time optimal control problems are a crucial type of control problems that deal with a dynamic system evolving in discrete time-steps. This paper introduces a new technique for solving linear discrete-time optimal control problems with state delays, applicable to both finite and infinite time horizons. Our method employs a Riccati matrix equation, optimizing control strategies and ensuring system stability through bounded control inputs. We adopt a Bolza problem for the performance index, which guides the classification of control issues. The technique simplifies problems into manageable Riccati matrix equations using the Euler–Lagrange equations and Pontryagin maximum principle, ensuring stability and necessary condition compliance. The paper validates the approach with numerical examples from quantum mechanics and classical physics, demonstrating its practicality and potential for broader application. [Display omitted] • Deriving Discrete-Time Euler–Lagrange Equations for Linear Discrete-time Optimal Control Problems with a Constant Delay. • Investigating Stability and Controllability of Linear Discrete-time Optimal Control Problems with a Constant Delay. • Developing Open-Loop Control Approach for Effective Control Strategies. • Proposing a Closed-Loop Control Approach with Riccati Matrix Equation for Optimal Solutions. • Evaluating the Performance of Proposed Approach and Real-World Applications. [ABSTRACT FROM AUTHOR]

This paper focuses on secure consensus for leader-following multiagent systems (MASs) modeled by partial differential equations (PDEs) under denial of service (DoS) attacks. To mitigate the negative effects of DoS attacks, which can paralyze communication and cause agents to fail to receive valid control inputs, a buffer region is established in the communication channels among agents to temporarily store messages from neighbors. Additionally, since the states of the leader and followers are not always measurable, observers are used to estimate these states. To address these challenges, this paper proposes two boundary controllers to ensure leader-following consensus in both measurable and unmeasurable states. One controller is based on original boundary information, while the other utilizes observation information from both the leader and followers. To the best of our knowledge, this is the first attempt to use buffers to solve a class of PDEs-based MASs under DoS attacks. Furthermore, the boundary control approach has the potential to significantly reduce the number of actuators required, thereby lowering control costs. Finally, we present two numerical examples to validate the feasibility of the proposed methods. • The effects of time and position on state changes are considered to better reflect agents' behaviors. • Boundary control approach reduces the quantity of actuators used with lower control costs. • Buffer is used to defend against denial of service attacks. • The proposed method guarantees leader-following consensus for observer-based multiagent systems. [ABSTRACT FROM AUTHOR]

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 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]

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]

The time delay (TD) in the levitation control system significantly affects the dynamic performance of the closed-loop system in electromagnetic suspension (EMS) maglev vehicles. Excessive TD can cause levitation instability, making it essential to explore effective mitigation methods. To address this issue, a Smith Predictor (SP) is integrated into the traditional PID levitation control system. The combination of theoretical analysis and numerical simulation is employed to assess the stability of the time-delay levitation control system after the integration of the Smith Predictor. Theoretical analysis reveals that when TD exceeds a critical threshold, the levitation system becomes unstable. The addition of SP alters the root trajectory of the system characteristic equation from positive to negative, and recovers the levitation system to stable status. Assuming complete knowledge of the dynamic system, the TD compensation value in the SP becomes a key parameter that determines its performance. A minimum effective value (MEV) for TD compensation is identified, correlating with the system's stability region. Under the influence of TD, more complex systems and higher running speeds of the maglev vehicle lead to a narrower stable region and a larger MEV for TD compensation. Given the simulation parameters in this paper, with a system TD of 15 ms and a maximum vehicle speed of 160 km/h, the MEV for TD compensation in the SP should be set at 12 ms. • Smith Predictor is introduced into maglev control system to mitigate the adverse impact from TD. • Analyzed application value of Smith Predictor in levitation control system with time delay. • Provided suggestions for the broader implementation of Smith Predictor in practical engineering. [ABSTRACT FROM AUTHOR]

Global Nash equilibrium is an optimal solution for each player in a graphical game. This paper proposes an iterative adaptive dynamic programming-based algorithm to solve the global Nash equilibrium solution for optimal containment control problem with robustness analysis to the iterative error. The containment control problem is transferred into the graphical game formulation. Sufficient conditions are given to decouple the Hamilton–Jacobi equations, which guarantee the solvability of the global Nash equilibrium solution. The iterative algorithm is designed to obtain the solution without any knowledge of system dynamics. Conditions of iterative error for global stability are given with rigorous proof. Compared with existing works, the design procedures of control gain and coupling strength are separated, which avoids trivial cases in the design procedure. The robustness analysis exactly quantifies the effect of the iterative error caused by various sources in engineering practice. The theoretical results are validated by two numerical examples with marginally stable and unstable dynamics of the leader. [ABSTRACT FROM AUTHOR]

The paper focuses on the problem of controlling a double-unit actuating system with a series of actuators. Based on the conventional mid-ranging control system (MCS) with two PI controllers, a robust step-by-step design and tuning framework is proposed based on the D-partition method to improve the cooperation between the controllers. Furthermore, a universal modification is proposed that can improve the MCS performance by adding a feedforward compensator. Both numerical and experimental tests are conducted to validate the introduced concepts. The locally produced results show quantitative improvements over conventional solutions, which in selected instances reach between 16 % and 26 %, according to the user-defined integral quality criterion. • Double-unit actuators can improve control performance but require dedicated control. • Robust tuning of PI-based mid-range control system is proposed. • Control improvement is proposed by design of dedicated feedforward compensator. • PLC-based implementation and practical validation shows practical applicability. [ABSTRACT FROM AUTHOR]

The problem of asynchronous fault detection filtering for nonhomogeneous Markov jumping systems with dynamic quantization and hybrid cyber attacks is addressed in this paper. The introduction of polytopic-structure-based transition probabilities is employed to describe the nonhomogeneous Markov process. An asynchronous fault detection filter is proposed, which utilizes the hidden Markov model to achieve comprehensive access to the plant mode information. Prior to transmission to the filter, the measurement output of the system undergoes quantization using a dynamic quantizer. The novel hybrid cyber attacks model being discussed involves four types of attacks: deception attacks, denial-of-service attacks, no attack, and hybrid attacks with both deception and denial-of-service attacks. By constructing Lyapunov functional, sufficient conditions are presented for achieving the stochastic stability with H ∞ performance. Under the complex network environment, the industrial application of the presented asynchronous fault detection filtering model is demonstrated on a non-isothermal continuous stirred tank reactor. The simulation results confirm the practicality of the proposed design method. • The dynamic quantizer is adopted to quantify measurement output signals to reduce information exchange frequency and communication burden. • A convex polyhedron is used to describe the time-varying transition probability matrix. • Due to the mode information between the filter and the plant cannot be fully obtained in the networked control system, a HMM-based asynchronous mechanism is proposed. • In order to improve the system performance, a novel hybrid network attack model is proposed, which includes four attack modes. [ABSTRACT FROM AUTHOR]

In real industrial settings, collecting and labeling concurrent abnormal control chart pattern (CCP) samples are challenging, thereby hindering the effectiveness of current CCP recognition (CCPR) methods. This paper introduces zero-shot learning into quality control, proposing an intelligent model for recognizing zero-shot concurrent CCPs (C-CCPs). A multiscale ordinal pattern (OP) feature considering data sequential relationship is proposed. Drawing from expert knowledge, an attribute description space (ADS) is established to infer from single CCPs to C-CCPs. An ADS is embedded between features and labels, and the attribute classifier associates the features and attributes of CCPs. Experimental results demonstrate an accuracy of 98.73 % for 11 unseen C-CCPs and an overall accuracy of 98.89 % for all 19 CCPs, without C-CCP samples in training. Compared with other features, the multiscale OP feature has the best recognition effect on unseen C-CCPs. • Proposed a zero-shot learning framework for concurrent control chart pattern recognition of manufacturing process. • Established an attribute description space to represent semantic relationships among different CCPs. • Proposed an inference model for unseen concurrent CCPs based on attribute transfer. • Established a mapping between multi-scale OP features and CCP attributes. • GZSL-CCPR model achieved a recognition accuracy of 98.89 % for up to 19 types of CCPs. [ABSTRACT FROM AUTHOR]

This paper is devoted to the tracking control for an uncertain robotic system with both output constraint and dead-zone input. Remarkably, the distinctive characters of the system are reflected by system uncertainties and output constraint. First, more serious uncertainties are involved since unknown nonlinear dynamic matrices, external disturbance and the dead-zone input (see unknown slopes and break points therein) are simultaneously considered, but those of the related literature are not. Second, weaker conditions on the output constraint are allowed since the constraint functions considered are only first but not more order continuously differentiable while any their time derivatives are not necessarily available for feedback. This leads to the incapability of the traditional control schemes on this topic. To solve the control problem, a novel control framework is proposed based on time-varying feedback which overcomes the serious system uncertainties while relaxes the conditions on output constraints. Specifically, a state transformation with a time-varying gain is first introduced to derive a new system. Then, by using the traditional backstepping method with the introduction of the time-varying gain in the estimations of some uncertain terms, a time-varying feedback controller is explicitly designed, which ensures that all the states of the resulting closed-loop system are bounded while system output asymptotically tracks the reference signal without any violation of the output constraint. Finally, simulation results for two practical examples are provided to validate the effectiveness of the proposed theoretical results, and moreover, a comparison with PID method is given to show the superiority of the proposed method on tracking accuracy and robustness. • More serious uncertainties are involved in the system under investigation than those of the related literature. • Weaker conditions are allowed on the output constraint than those of the related literature. • A novel time-varying control scheme is proposed by introducing a time-varying gain in control design. [ABSTRACT FROM AUTHOR]

This paper presents a novel hierarchical control scheme for solving the data-driven optimal cooperative tracking control problem of heterogeneous multi-agent systems. Considering that followers cannot communicate with the leader, a prescribed-time fully distributed observer is devised to estimate the leader's state for each follower. Then, the data-driven decentralized controller is designed to ensure that the follower's output can track the leader's one. Compared with the existing results, the advantages of the designed distributed observer are that the prescribed convergence time is completely predetermined by the designer, and the design of the observer gain is independent of the global topology information. Besides, the advantages of the designed decentralized controller are that neither the follower's system model nor a known initial stabilizing control policy is required. Finally, simulation results exemplify the advantage of the proposed method. • Compared with the existing methods, the advantage of our proposed prescribed-time fully distributed observer is that the leader's state can be accurately estimated within the designer's predetermined time; • Unlike the existing RL-based algorithms that rely on the initial stabilizing control policy associated with the accurate system matrices, the proposed new RL-based algorithm not only does not require a known initial stabilizing control policy, but also ensures the optimal tracking control in a data-driven manner. • Different from the homotopy-based policy iteration algorithm, an improved counterpart is presented, where the design parameter can be directly designed in the data-driven manner. [ABSTRACT FROM AUTHOR]

In multi-mass systems, torsional vibration is a common and annoying phenomenon. Effective vibration suppression and robustness to wide-range parameter variations are essential for a sound motion system. However, most control methods focus on the primary resonance mode, and the high-order resonance modes are not actively treated in the control design, resulting in the control bandwidth not being high enough and limiting the control performance. This paper proposes a novel two-stage design scheme to realize a wideband control to improve control performance. First, a hybrid uncertainty model is tailored for multi-mass systems, which uses an equivalent and uncertain spring constant to describe the variation of the primary mode and a dynamic uncertainty to cover the other resonance modes. This hybrid model strikes a better balance between the model conservatism and the feasibility of a less conservative design. Then, the passivity of the parameter uncertainty is utilized to conduct a phase compensation on the nominal system. After the phase compensation, all uncertainties are converted into norm-bounded ones, and the robust performance design is carried out. This method is applied to vehicle drivetrain benches, and its superiority is validated through simulation comparisons and experiments on two typical types of drivetrain benches. • Hybrid model for multi-mass systems characterizes primary resonance and other modes. • Two-stage design method with phase-shaping achieves high performance robust control. • The method is applied to drivetrain benches, outperforming the conventional methods. [ABSTRACT FROM AUTHOR]

In this paper, an event-triggered adaptive neural prescribed performance admittance control (ETANPPAC) scheme is proposed to control the constrained robotic systems without velocity sensors. To ensure compliance during human–robot interaction, the reference trajectory is obtained by reshaping the desired trajectory for the robotic systems based on the admittance relationship, where a saturation function is used to constrain the reference trajectory, avoiding excessive contact forces that could render the trajectory inexecutable. Moreover, a barrier Lyapunov function is used to constrain the tracking errors for prescribed performance, where a velocity observer and a radial basis function neural network are designed to estimate the velocity and the uncertainty of the robotic systems, respectively, to enhance control performance. To reduce the communication burden, an event-triggered mechanism is introduced and the Zeno behavior is avoided with the event-triggered condition. The stability of the whole control scheme is analyzed by the Lyapunov function. Simulation and experimental tests demonstrate that the proposed ETANPPAC scheme can track the desired trajectory well under constraints and reduce the communication burden, thereby achieving better efficiency for controlling the robotic systems compared with similar control schemes. • The ETANPPAC scheme achieves compliance and prescribed performance with constraints. • Barrier Lyapunov function constrains tracking errors, achieving desired performance. • Event-triggered method reduces the system burden and the Zeno behavior is avoided. • The proposed ETANPPAC scheme is tested on a numerical model and actual manipulator. [ABSTRACT FROM AUTHOR]

Extensive researches have been conducted on transfer learning based fault diagnosis. However, negative information transfer may arise due to significant differences in the subdomain distribution across multiple source domains (MSDs). Most existing methods focus solely on the impact of subdomains from a single source domain (SSD) on the target domain (TD). Therefore, this paper proposed a novel multi-stage alignment multi-source subdomain adaptation (MAMSA) method. The global feature extractor is designed to extract domain-invariant features. Three domain-specific feature extractors capture high-level fault features from different domains with a customized adaptation strategy, which combines adversarial learning and distribution alignment based on multiple pseudo-label-guided local maximum mean discrepancy (MP-LMMD) to learn subdomain-invariant features. MP-LMMD utilizes pseudo-labels generated from all classifiers in the TD to guide the alignment of subdomains, suppressing negative transfer from the source domains (SDs). The experimental results indicate that the MAMSA method has excellent capabilities to suppress negative transfer, and the diagnostic performance can be greatly promoted with MAMSA under cross-working conditions. • A novel multi-stage alignment multi-source subdomain adaptation (MAMSA) method is proposed. • A two-stage feature extractor based on specially designed attention mechanism is proposed. • A multiple pseudo-label-guided local maximum mean discrepancy metric is proposed. • The proposed method can suppress the negative transfer and achieve good diagnostic results. [ABSTRACT FROM AUTHOR]

This paper proposes a model-based optimization method for the production of automotive seals in an extrusion process. The high production throughput, coupled with quality constraints and the inherent uncertainty of the process, encourages the search for operating conditions that minimize nonconformities. The main uncertainties arise from the process variability and from the raw material itself. The proposed method, which is based on Bayesian optimization, takes these factors into account and obtains a robust set of process parameters. Due to the high computational cost and complexity of performing detailed simulations, a reduced order model is used to address the optimization. The proposal has been evaluated in a virtual environment, where it has been verified that it is able to minimize the impact of process uncertainties. In particular, it would significantly improve the quality of the product without incurring additional costs, achieving a 50% tighter dimensional tolerance compared to a solution obtained by a deterministic optimization algorithm. • Optimizing automotive seal production with a model-based approach. • A reduced order model (ROM) is used for efficient optimization. • Uncertainties in the model and the system itself are taken into account. • Bayesian optimization is applied for robust parameter determination. • A 50% tighter dimensional tolerance is achieved compared to a deterministic solution. [ABSTRACT FROM AUTHOR]

Reliable and precise straightness profile measurements are crucial for manufacturing ultra-precision components and are capable of further enhancing their accuracy. The Fourier three-probe (F3P) straightness measurement allows for precise assessment of the workpiece profile on the machine by eliminating the harmful influence of the error motion of the sliding table. However, the probe spacing uncertainty deteriorates the measurement accuracy remarkably; and, the affecting mechanism behind this phenomenon has not yet been studied in detail. In this context, this paper thoroughly investigated the propagation of the probe spacing uncertainty in the F3P measurement. First, the influence of the probe spacing deviation is analyzed. Next, by calculating the partial differential of Laplace transform of the workpiece profile, we algebraically deduce the probe spacing uncertainty propagation law, especially in the harmonic domain. Subsequently, Monte Carlo simulations are carried out to confirm the derived propagation law. To reduce uncertainty propagation, a hybrid approach is presented: (I) F3P measurements are carried out under changing probe spacings to produce several sets of Fourier coefficients; (II) optimal harmonic estimates are selected individually according to the harmonic uncertainty. Finally, simulations and experimental measurements are performed for verification. • Spacing uncertainty propagation law in F3S straightness measurement is deduced. • Monte Carlo method is adopted to confirm the derived propagation law. • Hybridization in harmonic domain is proposed to reduce uncertainty propagation. • The hybrid method enables reduction of both random and systematic uncertainties. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive dynamic surface (DSC) guidance law for missile is designed to intercept the maneuvering target with field-of-view (FOV) and terminal angle constraints in three-dimensional(3D) space, and the missile autopilot dynamics is considered. Firstly, the time-varying transformation function related to line of sight (LOS) is used to replace the FOV constraints, transforming the process-constrained control problem into the output-constrained control problem. Meanwhile, the 3D coupled relative kinematics model considering missile autopilot dynamics and maneuvering target acceleration is established. Secondly, a novel time-varying asymmetric barrier Lyapunov function (TABLF) with dead-zone characteristics is introduced to the adaptive dynamic surface guidance law design process to improve the robustness of parameter debugging. Thirdly, with the help of a nonlinear adaptive filter, the 'explosion of complexity' problem can be avoided effectively, which is caused by analytic computation of virtual signal derivatives. Furthermore, aiming at the problem of autopilot dynamic errors, target acceleration disturbances, and unmeasurable parameters in the model, a novel adaptive law is used to evaluate online. Then, the stability of the closed-loop system is rigorously proven using Lyapunov criteria. Ultimately, Numerical simulations with various constraints and comparison studies have been considered to show the feasibility and effectiveness of the proposed missile guidance law. • The terminal angle constraint guidance (TACG) law within field of view (FOV) constraints is developed in the three-dimensional(3D) space while considering the autopilot dynamics and the maneuvering target acceleration simultaneously, which can promote the guidance accuracy. • The utilization of the novel FOV constraint transformation can transform the FOV constraints into the output constraints, which facilitates the derivation of adaptive dynamic surface control (DSC) scheme. • The time-varying asymmetric barrier Lyapunov function (TABLF) with the dead zone characteristic is designed to derive the 3D TACG law with FOV constraints, which can enhance the accuracy of convergence and improve the robustness of parameter debugging. • The 'explosion of complexity' is avoided by using the novel nonlinear adaptive filter. In addition, a novel adaptive law is designed to adapt the unknown parameters automatically containing autopilot modeling error, target acceleration disturbance, and unmeasurable parameters. [ABSTRACT FROM AUTHOR]

The increasing role of unmanned aerial vehicle (UAV) swarms in modern warfare poses a significant challenge to ground and air defense systems. Considering complex terrain environments and multi-sensor resources including radar and photoelectric systems constraints, a novel multi-sensor dynamic scheduling algorithm is proposed in this paper. Firstly, a transmission model with Fresnel zone under complex terrain and sensor models for radar/photoelectric systems are established. Considering the constraints of 6 factors, such as pitch angle, array scanning angle and threat levels, a detection model is developed subsequently. Secondly, to meet the real-time requirements of ground and air defense systems, a fast calculation method for Fresnel zone clearance using adaptive buffer is achieved. Thirdly, an improved Hungarian algorithm is proposed to solve the combinatorial optimization problem of sensor scheduling. Finally, simulation experiments are conducted to evaluate the algorithm performance under different conditions. The results demonstrate that the proposed approach significantly reduces the sensor switching rate while achieving a high sensor-UAV matching rate and high-threat matching rate. Furthermore, the simulation results verify the effectiveness of the proposed algorithm when applied to multi-sensor scheduling for defending UAV swarms. • Modern militaries need advanced sensor scheduling solutions to defend UAV. • Considering the Fresnel zone under complex terrain is crucial. • Adaptive buffer enables rapid calculation of Fresnel zone clearance. • Multiple sensor constraints make scheduling problems realistic. • Improved algorithm effectively enhances matching rates while reducing switching. [ABSTRACT FROM AUTHOR]

Aiming to address soft sensing model degradation under changing working conditions, and to accommodate dynamic, nonlinear, and multimodal data characteristics, this paper proposes a nonlinear dynamic transfer soft sensor algorithm. The approach leverages time-delay data augmentation to capture dynamics and projects the augmented data into a latent space for constructing a nonlinear regression model. Two regular terms, distribution alignment regularity and first-order difference regularity, are introduced during data projection to address data distribution disparities. Laplace regularity is incorporated into the nonlinear regression model to ensure geometric structure preservation. The final optimization objective is formulated within the framework of partial least squares, and hyperparameters are determined using Bayesian optimization. The effectiveness of the proposed algorithm is demonstrated through experiments on three public datasets. • Adaptive soft sensor combines unsupervised domain adaptation and dynamic PLS for variable conditions. • Leverages time-delay data augmentation to capture dynamics and constructs a nonlinear regression model in a latent space. • Introduces regular terms for distribution alignment and first-order differences, and incorporates Laplace regularity for geometric structure preservation. • Optimization within partial least squares framework, hyperparameters tuned by Bayesian optimization. [ABSTRACT FROM AUTHOR]

The development of microgrid automation depends on information and communication technologies, which are vulnerable to cyber-attacks. Recent advancements in MGs enhance power systems' efficacy and reliability, but cybersecurity remains a significant concern, especially with false data injection attacks (FDIAs) posing serious threats. FDIAs can compromise measurement devices and tamper with State Estimation (SE), risking the seamless operation of MGs. To address this, this paper proposes an efficient Iterative Free Detection of False Data (IFDFD) scheme for detecting FDIAs in microgrid state estimation. The IFDFD scheme uses complex Micro Phasor Measurement Unit (μPMU) measurements and computes nodal power injections to detect FDIAs. Furthermore, the proposed scheme integrates an S-Estimator to eliminate noise errors caused by environmental factors and the component lifespan, making IFDFD robust against sophisticated attackers. The proposed IFDFD scheme has been tested and validated on the modified IEEE 14 bus test system, integrating Distributed Generations (DGs). False data was injected into the measurements to test the scheme's effectiveness. The efficacy of proposed IFDFD scheme has been validated by comparing it to existing method of FDIAs. The obtained result clearly validates the efficacy of the proposed IFDFD scheme. • Addresses cybersecurity concerns in MG automation dependent on Information and Communication Technology. • Proposed IFDFD scheme detects FDIAs using μPMU measurements and nodal power injections. • IFDFD integrates S-Estimator to eliminate noise errors from environmental factors. [ABSTRACT FROM AUTHOR]

This paper studies a safe model predictive control (MPC)-based disturbance rejection control for a broad range of uncertain nonlinear systems subject to complex state safety constraints. The system under study is composed of a nominal model and an uncertain term that encapsulates modeling uncertainty, control mismatch, and external disturbances. In order to estimate the system state and total uncertainty, an extended state observer (ESO) is first designed. Utilizing the output of the ESO, the control compensates for the total uncertainty in real time and concurrently implements a control barrier function (CBF)-based MPC for the compensated system. The proposed control framework guarantees both safety and disturbance rejection. Compared to the baseline algorithm CBF-MPC, the proposed method significantly enhances system stability with a smaller root mean square (RMS) error of the system state from the equilibrium point. Rigorous theoretical analysis and simulation experiments are provided to validate the effectiveness of the proposed scheme. • A safe control scheme for uncertain nonlinear systems subject to complex state safety constraints. • Extended state observer (ESO) for estimating the system state and disturbance. • Control barrier function (CBF)-based model predictive control (MPC) for guaranteeing the safety constraints. • Sufficient conditions are established for maintaining stability, feasibility and safety of the closed-loop system. • The scheme manifested high performance in the application of adaptive cruise control. [ABSTRACT FROM AUTHOR]

In this paper, the problem of highly performance motion control of tank bidirectional stabilizer with dead zone nonlinearity and uncertain nonlinearity is addressed. First, the electromechanical coupling dynamics model of bidirectional stabilizer is developed finely. Second, the dead zone nonlinearity in bidirectional stabilizer is characterized as the combination of an uncertain time-varying gain and a bounded disturbance term. Meanwhile, an adaptive robust controller with dead zone compensation is proposed by organically combining adaptive technique and extended state observer (ESO) through backstepping method. The adaptive technique is employed to reduce the impact of unknown system parameter and dead zone parameter. Furthermore, the ESO is constructed to compensate the lumped uncertainties including unmodeled dynamics and dead zone residual, and integrated together via a feedforward cancellation technique. Moreover, the adaptive robust control law is derived to ensure final global stability. In stability analysis, the asymptotic tracking performance of the proposed controller can be guaranteed as the uncertainty nonlinearities in tank bidirectional stabilizer are constant. It is also guaranteed to achieve bounded tracking performance when time-varying uncertainties exist. Extensive co-simulation and experimental results verify the superiority of the proposed strategy. • A mechatronic dynamics model of the tank bidirectional stabilizer is developed, which is accurately modeled including couplings, nonlinearities and uncertainties. • The parametric adaptive function is constructed to update the unmeasurable dead zone parameters online, and the extended state observation technique is introduced to eliminate the dead zone nonlinearity and unmodeled dynamics. • The Lyapunov stability analysis demonstrates that the asymptotic tracking capability is guaranteed when the system uncertainty is constant, and the uniformly bounded tracking performance is guaranteed owing to the existence of time-varying uncertainties. • Comparative analysis of co-simulation and experiment confirms the effectiveness and feasibility of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper investigates the estimation problem of a class of nonlinear systems with actuator and sensor faults. The primary objective is to design a distributed fault-tolerant observer which can estimate system states and actuator faults. Firstly, a distributed observer network with intermediate parameters is constructed to compensate the missing information of unobservable nodes of the system. Next, a class of redundant sensors is set up for each distributed observer node to obtain more output measurement samples. More importantly, when some of the redundant sensors occur faults, all sensor signals will be further processed and classified by a new algorithm. An index of sensor health level is constructed to characterize the quality of the fault-free or faulty sensor signals. By using the proposed algorithm, unhealthy sensor signals will be automatically filtered out, while healthy ones will be retained. Based on the healthy sensor signals, the system states and actuator faults are estimated. Finally, an example demonstrates that the proposed method is effective. • In this article, an improved distributed observer network is designed. The actuator fault can be estimated even if the sub-observer nodes are not observable. • We set up a class of redundant sensors to obtain more output measurement samples. Then, a novel algorithm is designed to filter out low quality output information. • We introduce a novel robustness term to against the effect of faulty sensors. The actuator fault can be observed in the case of sensor fault, which does not require employing extra fault detection mechanisms. [ABSTRACT FROM AUTHOR]

This paper introduces a novel droop-based decentralized control scheme to address the power-sharing challenges within a PV-fed islanded AC microgrid. This novel approach integrates both conventional (P-f/Q-V) and virtual impedance concepts to optimize and manage the precise distribution of active and reactive power among parallel operating inverters posing a significant research challenge. The conventional droop control methods encounter limitations such as voltage and frequency deviations and inaccuracies in power-sharing due to line impedance disparities. To overcome these limitations, the proposed solution integrates an enhanced virtual impedance control loop alongside the conventional control loop (P-f/Q-V). The efficacy of this approach is showcased through simulations conducted using the OPAL-RT OP4510 simulator within the MATLAB/Simulink platform. The Real-time simulation outcomes confirm the efficiency of the suggested control strategy, guaranteeing precise distribution of both active and reactive power while upholding stable voltage and frequency profiles within the system. • A novel virtual impedance structure for improved power sharing capability among parallel inverters. • PCC voltage stability under load change and line impedance mismatch scenarios. • Restoration of AC MG frequency under load transient condition. • An analytical solution for choosing the equivalent output virtual impedance. [ABSTRACT FROM AUTHOR]

The paper proposes a WADC approach using CART technique to dampen inter-area oscillations (IAOs) in bulk power systems. In this case, PMU data are filtered to estimate inter-area dynamics in which using pade approximation, a pole-zero IAO compensation block is designed. An online random decrement technique is also developed to identify the coherent groups and damping ratios to activate the WADC for oscillation damping. An offline process is provided to identify 200 critical IAO contingencies and tunes WADC gains using PSO for training CARTs via a set of 200 input inter-area signals and assigning output controlling gains pre-trained data and evaluating the CART estimations through online operation. The WADC approach is validated for oscillation damping on a 39-bus system and a realistic 561-generator Iranian grid. Simulations show 98 % accuracy in achieving sufficient damping ratios (>0.6) across various operating conditions. • A new approach based on WADC is proposed for damping inter-area oscillations. • A new scheme based on individual WADC for each generator is proposed. • CART technique is used for estimating optimal gains of individual WADC controller. • A combination index is proposed to find the most effective individual WADCs. [ABSTRACT FROM AUTHOR]

To address the problem of underactuated surface vessel (USV) formation control in static obstacle environments with model uncertainties and time-varying external disturbances, a model-free formation control strategy is proposed in this paper. First, based on the guiding vector field (GVF), a composite GVF is developed to guide USV formation to the desired position and to avoid multiple static obstacles. Second, a flexible constraint strategy is introduced, and the constraint boundary conditions are appropriately relaxed to avoid singularities in the obstacle environment. Then, based on the Mexican hat wavelet function, the self-structuring fuzzy Mexican hat wavelet cerebellar model articulation controller (SCMAC), and a self-structuring fuzzy Mexican hat wavelet brain emotional learning controller (SBELC), are proposed to achieve model-free control. In addition, the self-structuring algorithm is embedded into SCMAC and SBELC to achieve autonomous optimization of the controller structure and to reduce the computational effort of the control system. The salient features in the proposed control strategy are as follows. First, the proposed model-free formation control strategy does not have to rely on accurate model information. Second, collisions are effectively avoided, and good control performance is guaranteed even under the influence of disturbances and static obstacles. Third, the proposed self-structuring algorithm achieves automatic construction of the controller structure. Finally, the signals in the control system are proven to be bounded, and the simulation results verify the feasibility and superiority of the proposed model-free control strategy. • An adaptive intelligent formation control method is proposed based on SCMAC and SBELC. • A Mexican hat wavelet function is developed to enhance the ability of the formation control system to process signals. • The GVF and flexible constraints strategy are introduced to avoid static obstacles and ensure control performance. • A self-structuring algorithm is embedded to optimize the structure of SCMAC and SBELC. [ABSTRACT FROM AUTHOR]

In this paper, the problem of adaptive neural network prescribed performance tracking control for a class of non-strict feedback time-delay systems constrained by full-state is studied. Radial basis function (RBF) neural networks (NNs) are integrated into the backstepping medium to deal with the uncertain functions and the barrier Lyapunov function (BLF) technique ensures that the state of the system does not exceed its limits. Subsequently, integrated with the Lyapunov–Krasovskii functional, the proposed control scheme makes the tracking errors converge to the preset region while the state constraint is not violated. Finally, the effectiveness of the scheme is supported by two simulation experiments. • An adaptive neural control scheme is proposed for nonlinear time-delay systems. • For unknown model, the proposed controller can achieve the control objective. • Coordinate transformation is used to improve system tracking performance. [ABSTRACT FROM AUTHOR]