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• A preassigned finite-time attitude synchronization controller is proposed for multiple rigid spacecraft systems by using two designed barrier Lyapunov functions. • The convergence time of the designed controller can be preassigned according to different space task requirements and it is independent of the initial condition of the system states and control parameters. • The steady-state error of the closed-loop system can be limited to a certain range by the appropriate choice of the controller parameters. This paper investigates the preassigned finite-time attitude synchronization control for the spacecraft formation with a dynamic virtual leader under an undirected connected graph. Each follower is described by a rigid spacecraft model subjects to parameter uncertainty, actuator saturation, and the disturbance. First, two fixed-time sliding mode estimators are introduced to estimate the reference attitude variable and the reference angular velocity, respectively. Then, by selecting the attitude errors as the system states, an attitude tracking error system for the spacecraft formation is established. An extended state observer is applied for computing the total disturbance in the attitude tracking error system. Based on the estimations of the extended state observer and two sliding mode estimators, a novel distributed controller is proposed to force the states of the established attitude tracking error system converge to a small region in preassigned finite time by using two modified time-varying barrier Lyapunov functions. The practical preassigned finite-time stability of the resulting closed-loop system is proven by Lyapunov stability theory. Finally, some simulations are provided to illustrate the effectiveness and advantages of the developed preassigned finite-time attitude synchronization control scheme. [ABSTRACT FROM AUTHOR]

Maximum power point tracking (MPPT) controller is the main element in photovoltaic (PV) systems, which is used to ensure maximum power extraction under different meteorological conditions. A MPPT controller can guarantee good performance criteria even in the presence of climatic changes. To achieve this goal, several techniques have been proposed in the literature to improve robustness of the PV system control, such as artificial intelligence and multiswarm particle swarm optimization (MSPSO) algorithm. Previous research on classical MSPSO has shown that the algorithm search behavior cannot find the optimal solution for certain problems. In this context, we investigate the design of a new MPPT controller based on a modified version of heterogeneous multiswarm particle swarm optimization algorithm using an adaptive factor selection strategy (FMSPSO) for PV systems. The proposed FMSPSO can improve the tracking capability with high accuracy, less oscillations, and high robustness. To validate the proposed solution, a simulation and experimental benchmarking of a PV system are presented and analyzed. The obtained results show the effectiveness of the proposed solution compared with the classical MSPSO, fuzzy logic, and perturb and observe (P&O) control presented in other recent works. [ABSTRACT FROM AUTHOR]

The operation of the three-phase standalone inverter is affected by many factors, such as heavy changes in load, unbalanced loads, nonlinear loads, system uncertainties and external disturbances. These are critical weaknesses for the control system of the three-phase inverter to reach robust optimal performance. This paper proposes an adaptive optimal sliding mode control (AOSMC) scheme for a three-phase nonlinear uncertain inverter. This AOSMC strategy solves the problems of nonlinear optimization by adaptive dynamic programming, one of the techniques of reinforcement learning, and overcomes the uncertainties and disturbance effects by a disturbance observer–based sliding mode controller. This algorithm uses only one neural network to approximate the critic; therefore, the burden of computation is significantly reduced. Both the weight matrix of the critic network and the disturbance observer are asymptotically stable. The overall system is guaranteed to be ultimately uniformly bounded stable via Lyapunov stable theory. The simulation is conducted to validate the insensitivity of the proposed AOSMC algorithm to working conditions. Also, the competitive results are presented to demonstrate the improvement of the proposed AOSMC scheme in comparison to some other existing controllers. [ABSTRACT FROM AUTHOR]

In this paper, a LuGre model–based robust adaptive control (RAC) approach is presented for a pump-controlled hydraulic actuator. We first decompose the LuGre friction model into its steady-state model and a lumped dynamic part applying the mean value theorem, which are compensated by a feedforward term and a robust adaptive term, respectively. The robust adaptive term also plays a part in mismatched disturbance attenuation. In addition, parametric uncertainties and matched disturbances are handled by σ -modified adaptation laws and a robust control law, respectively. The stability of the closed-loop system is proved via the Lyapunov analysis. The efficacy and robustness of the proposed approach are validated by comparative experiments. Compared with common adaptive friction compensation methods, the proposed method has a simpler structure, less computational burden, better control performance, and stronger robustness. Moreover, since the available information is separated from the LuGre model and acts as a model-based compensation term, the design conservativeness of RAC is effectively reduced. [ABSTRACT FROM AUTHOR]

The paper studies a presynchronization control of grid connection for large merchant marine microgrid inverters. We present a virtual synchronous generator (VSG) algorithm with model-free adaptive control (MFAC) to optimize the stable grid connection of ship microgrid and shore-to-ship power. To solve poor precision of presynchronization control under nonideal ship microgrid condition, an MFAC controller and its presynchronization method are developed for grid connection of ship-distributed generation inverters. The proposed presynchronization control method effectively avoids a high transient overcurrent and achieves a seamless grid connection to different types of shore power. The simulation results verify the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

The dynamic measurement of position and attitude information of a long‐distance moving object is a common demand in ground testing of aerospace engineering. Due to the movement from far to near and the limitations of camera resolution, it is necessary to use multi‐binocular cameras for segmented observation at different distances. However, achieving accurate and continuous position and attitude estimation is a challenging task. Therefore, this paper proposes a dynamic monitoring technique for long‐distance movement based on a multi‐binocular videogrammetric system. Aiming to solve the problem that the scale in images changes constantly during the moving process, a scale‐adaptive tracking method of circular targets is presented. Bundle adjustment (BA) with joint segments using an adaptive‐weighting least‐squares strategy is developed to enhance the measurement accuracy. The feasibility and reliability of the proposed technique are validated by a ground testing of relative measurement for spacecraft rendezvous and docking. The experimental results indicate that the proposed technique can obtain the actual motion state of the moving object, with a positioning accuracy of 3.2 mm (root mean square error), which can provide a reliable third‐party verification for on‐orbit measurement systems in ground testing. Compared with the results of BA with individual segments and vision measurement software PhotoModeler, the accuracy is improved by 45% and 30%, respectively. [ABSTRACT FROM AUTHOR]

The interaction force control problem for the object manipulated by the dual-arm manipulator with the unknown environment is addressed in this paper. An adaptive dual-loop impedance control scheme is designed to meet the three control targets, including the trajectory tracking control of the object, the internal force control of the object with the two arms, and the interaction force control of the object with the unknown environment. Firstly, an adaptive variable impedance control method is proposed in the outer loop to copy with the unknown stiffness and position of the environment. Secondly, an impedance control scheme based on the internal force is designed in the inner loop to plan the internal force and the modified position supplied with the outer-loop control. Thirdly, to realize the reference trajectory supplied with the inner-loop control, the virtual decomposition control (VDC) method is incorporated, which is a recursive adaptive control progress, so that the unknown dynamics of the manipulators is acceptable and the computation complexity is also very low to satisfy the real-time control requirement. The stability verifications of the inner-loop impedance control and the VDC method are given based on the Lyapunov method, and the stability analysis of the outer-loop impedance control is obtained using the Routh criterion. Finally, a plane dual-arm robotic manipulator with three degrees of freedom is used to verify the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

The nonlinear system control is a classical problem in control engineering. In this paper, rather than try to get accurate nonlinear dynamics, the nonlinear and uncertain dynamics are viewed as a signal. It can be estimated by an extended state observer, and compensated by a control law. Accordingly, the nonlinear uncertain system is linearized. Based on the linearized system and the key point of the U-model control, a controller can be designed to obtain predetermined closed-loop system dynamics. To get a more satisfactory performance, a compensation signal of the total disturbance estimation (CSTDE) is designed. Based on the CSTDE, a compensation of the total disturbance estimation based extended state observer (CTDESO) and a fast-response active disturbance rejection control (FRADRC) are proposed. Convergence of the CTDESO and the closed-loop stability of the FRADRC are analyzed. Four nonlinear systems are considered to testify the proposed approaches. Numerical results show that, no matter disturbances exist or not, the proposed CTDESO can linearize a nonlinear system better, and the predetermined closed-loop responses can also be achieved more satisfactorily by the FRADRC. [ABSTRACT FROM AUTHOR]

Summary: The concerns of this article is to investigate the fixed time function combination synchronization (FCS) among three different chaotic systems with disturbances. First, a new stability theorem is presented by virtue of the integral inequality technique and a more accurate upper estimation of the convergence time is given. Next, the fixed time FCS problem is considered for the perturbed chaotic systems in which the scaling functions are generated by the bounded dynamical systems. Besides, the synchronization time is related to the control parameters rather than the initial values. The time upper bound can be calculated out by the simple formula, which is the function of control parameters. Finally, a simulation example is provided to illustrate the correctness of the derived criteria. [ABSTRACT FROM AUTHOR]

Summary: In this paper, the fixed‐time anti‐synchronization and synchronization fault‐tolerant control problem is considered for two identical Liu‐Chen‐Liu chaotic systems with uncertain parameters. With the help of fixed‐time stability theory and adaptive backstepping method, we propose two novel adaptive controllers. In the control scheme proposed in this paper, the adaptive backstepping technique is used to deal with the unknown parameters contained in the chaotic systems. Besides, the piecewise functional method is used to solve the singularity problem in the controller design process. The designed controllers achieve anti‐synchronization and synchronization of two identical Liu‐Chen‐Liu chaotic systems within the preset time interval. Simulation results are conducted to show the effectiveness of two proposed adaptive controllers. [ABSTRACT FROM AUTHOR]

Summary: False data injection attacks can disrupt the steering control actions and make a real threat to both the security and safety of autonomous vehicles. In this paper, a secure event‐triggered lateral control approach of autonomous vehicles subject to actuator attacks is investigated. Firstly, an arbitrary unknown actuator attacks is considered in the secure lateral steering control of autonomous vehicles. Thus, to save communication resources for the limited bandwidth CAN bus, the periodic event‐triggered transmission scheme is utilized, transforming the established lateral steering control into a time‐delay system through the consideration of periodic event‐triggered sampling. Then, an adaptive control compensation scheme is developed to mitigate the malicious effects caused by actuator attacks. The proposed secure control approach is skilled in compensating the unknown attacked steering control actions in an adaptive way. The stabilization criteria under the adaptive secure control law is well derived by Lyapunov–Krasovskii method and some linear inequality matrices operations. At last, the effectiveness of the proposed secure control scheme is verified by some numerical experiments borrowed from a practical vehicle. [ABSTRACT FROM AUTHOR]

Summary: This article studies robust adaptive control for a class of autonomous vehicle platoons. In particular, two innovative adaptive control laws are proposed to address both position and velocity tracking for a vehicle platoon. In addition, it is shown that robust asymptotic string stability can be delivered by the underlying adaptive control laws for the vehicle platoon, in the sense that these adaptive control laws are capable of mitigating parameter uncertainties involved in the nonlinear vehicular dynamics and achieving truly cooperative adaptive cruise control while ensuring the required safety spacing between each neighboring vehicle pair asymptotically. It is also shown that the control performance of the vehicle platoon can be further improved if the operating equilibrium points of all vehicles can be adaptively estimated, leading to two linear time‐invariant control laws for individual vehicles under both position and velocity controls and for the vehicle platoon. Simulation studies illustrate the effectiveness of the proposed control method, validating the results obtained for the class of vehicle platoons. [ABSTRACT FROM AUTHOR]

Summary: Supervisory control has been shown to be a very effective approach to adaptive control which ensures step‐tracking, exponential stability, and a degree of robustness to unmodeled dynamics. Here we apply the technique to the classical d$$ d $$‐step‐ahead adaptive control problem: we not only prove exponential stability and tracking of a general bounded reference signal, but also a never‐before‐seen linear‐like convolution bound on the effect of the noise/disturbance. This latter property is then leveraged to not only prove robustness to a degree of unmodeled dynamics, but also to a never‐before‐seen tolerance to a degree of nonlinearities and to slow (on average) plant parameter variations. [ABSTRACT FROM AUTHOR]

Summary: This paper studies the adaptive synchronization of complex spatio‐temporal networks modeled by semi‐linear hyperbolic partial differential equations (CSTNSLHPDEs) as well as considering time‐invariant and time‐varying delays in a one‐dimensional space. Firstly, a distributed adaptive controller is proposed, where different nodes are with different adaptive gains. Secondly, four cases, CSTNSLHPDEs with time‐invariant delays and one single weight, with time‐invariant delays and multi‐weights, with time‐varying delays and one single weight, and with time‐varying delays and multi‐weights, are successively analyzed, and synchronization conditions of these four cases are obtained by using the proposed distributed adaptive controller. In the end, examples illustrate the effectiveness of the proposed distributed adaptive controller. [ABSTRACT FROM AUTHOR]

Aiming to the sudden change of the system actual output constraint, a novel safe tracking control strategy is investigated for a class of non-strict feedback nonlinear systems with unknown initial tracking condition in this paper. A secure boundary protection method (SBPM) based on prescribed performance and a new prescribed finite-time performance function (PFTPF) are proposed. The SBPM can automatically construct two adjustable secure boundaries, so that the system output meets both the control performance prescribed by the PFTPF and the secure boundary constraint. In order to solve the problem of the excessive control input jitter caused by the sudden change of output constraint, a bidirectional filtering smoothing mechanism (BFSM) is proposed. The proposed SBPM can effectively ensure the safe tracking control of the system with unknown initial tracking condition when the actual output constraint changes suddenly, and ensure that the system has the prescribed finite-time control performance given by the PFTPF. Differently from the existing works, the system output will be strictly constrained into the actual output constraint without the prediction for the system output value. Finally, the effectiveness of this proposed method is verified by simulations. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive optimal control strategy of finite-time control for high-order uncertain strict-feedback nonlinear systems. Firstly, a reinforcement learning (RL) based an optimal control scheme is employed to design a optimal controller, to achieve global optimisation. Additionally, considering the unmeasurable states, we construct a fuzzy observer and utilise fuzzy logic systems to approximate the unknown functions. Meanwhile, the inclusion of command filtering and time-based control simplifies the controller design and enhances the system's response rapidity. Finally, the effectiveness and feasibility of the proposed approach are validated through a numerical simulation and a single link-robot system simulation. [ABSTRACT FROM AUTHOR]

This paper concerns the problem of asymptotic attitude stabilization for uncertain spacecraft subject to actuator constraints and bounded external disturbances. A simple model‐free saturated robust PD control is proposed within the framework of PD design methodology combining sliding mode control strategy. Asymptotic stabilization stability is proved in terms of Lyapunov's direct method and Barbalat's lemma. Advantages of the proposed control include the easy‐going implementation with simple and intuitive structure and explicit tuning procedure and abilities to guarantee the asymptotic attitude stabilization and bounded disturbance rejection and ensure the actuator constraint is not violated by selecting the control gains a priori. Simulation comparisons demonstrate the improved performance of the proposed approach. [ABSTRACT FROM AUTHOR]

Summary: This paper designs a distributed dynamic‐detection‐based event‐triggered control method to address the secure tracking control problem. A distributed online iterative dynamic detection mechanism is designed to detect denial‐of‐service (DoS) attacks. The detection results are transmitted into a proposed ET method to adjust the triggering threshold, which can mitigate the impact of DoS attacks. Moreover, the signals of state, observations of disturbance observers and adaptive laws are calculated to generate the trigger signals in the control signal simultaneously, which can help conserve communication resources and reduce the occurrence of pulse phenomenon. Meanwhile, since the predictor exists certain prediction errors that will reduce the tracking performance, a speed function is introduced to ensure the achievement of the control objective. The designed solution guarantees semiglobally uniformly ultimately bounded for all signals within the closed‐loop system. Finally, the effectiveness of control scheme is demonstrated through several simulation results. [ABSTRACT FROM AUTHOR]

This article considers the distributed secure consensus tracking control problem for multi‐agent systems (MASs) with relative output information. To tackle the potential false‐data‐injection (FDI) attacks on both the communication between physical components and the control center at the agent level, as well as the message transmission between connected agents, local sliding mode observers (SMO) and distributed observers are proposed for each agent. By introducing reconstructed signals for attacks and smooth terms to compensate the unknown input in the leader agent's dynamics, a distributed adaptive consensus protocol with relative output information is proposed. The closed‐loop stability is analyzed by introducing a novel Lyapunov function. It is shown that all closed‐loop signals are globally uniformly bounded and asymptotically consensus tracking can be achieved. Simulation results are provided to validate the theoretical findings. [ABSTRACT FROM AUTHOR]

This article studies the problem of prescribed‐time constrained tracking control for a class of 2×2$$ 2\times 2 $$ hyperbolic partial differential equation (PDE) systems with actuator dynamics, which are described by a set of nonlinear ordinary differential equations (ODEs). Since the control input only appears in the ODE subsystem rather than directly on the boundary of PDE subsystem, the control task becomes quite challenging. The most important is that for the control of such ODE controlled PDE systems we mainly make the following two contributions: (1) the controlled output of the PDE system tracks the reference signal within the prescribed time; (2) the controlled output and all the actuator states are constrained. It is the first time that such a prescribed‐time constrained tracking control problem is addressed for the PDE‐ODE coupled system considered in this article. Through rigorous theoretical proof, it is demonstrated that all the system states and control signals are bounded and sufficiently continuous by configuring appropriate design parameters. Finally, the performance is investigated via numerical simulation. [ABSTRACT FROM AUTHOR]

In this article, by creating a novel distributed full‐form dynamic linearization based model‐free adaptive containment control (FFDL‐MFACC) approach, the containment control problem of networked heterogeneous autonomous surface vehicles (ASVs), suffering from complex external disturbances and unavailable kinetic model‐information, is resolved. In light of the data‐driven strategy, a full‐form dynamic linearization‐based data model is efficiently established. Then, by further deploying the commonly used rotation matrix, a distributed FFDL‐MFACC scheme is thereafter developed such that accurate tracking of a predefined convex hull spanned for all follower vehicles can be achieved. Afterwards, a disturbance observer is further designed to accurately estimate the lumped disturbances and the estimation is served as a compensation within the distributed full‐form dynamic linearization. Rigorously theoretical analysis indicates the devised distributed FFDL‐MFACC method can ensure the asymptotic containment tracking of networked heterogeneous ASVs. Finally, simulation results are illustrated to verify the advantages of the devised approach. [ABSTRACT FROM AUTHOR]

This article presents an adaptive controller tailored for discrete‐time systems, emphasizing robustness and addressing time‐varying constraints. An equivalent model is constructed solely from input‐output data collected from an unknown plant. Performance optimization involves determining an appropriate learning rate guided by theoretical analysis. A control law is derived from the equivalent model and a proposed auxiliary controller, with parameter settings guided by theoretical analysis to achieve robust convergence in closed‐loop performance. Comprehensive experimental and comparative assessments confirm the superior performance of both the equivalent model and the proposed controller, especially in brushless DC motor position control. Moreover, compared to similar schemes, the proposed controller generates fewer high‐frequency control effort components. The investigation validates the approach's robustness in real‐world conditions, including uncertainties, disturbances, and common time‐varying constraints in practical plant scenarios. [ABSTRACT FROM AUTHOR]

This paper investigates the prescribed performance adaptive fault‐tolerant control problem for nonlinear systems with actuator faults and input dead‐zone (IDZ). The partial loss of effectiveness fault and bias fault are considered. First, the asymmetric nonlinear IDZ is transformed into an affine function in the backstepping process. Second, the radial basis function neural network technique is applied to deal with the unknown uncertainty of the studied system. The state observer is designed to estimate the unmeasurable states in the nonlinear systems. Third, a fault‐tolerant controller is developed such that all signals are semi‐globally uniformly ultimately bounded for the closed‐loop system, and the tracking error remains within the specified performance requirements even though the actuator faults and IDZ occur. Finally, an electromechanical system example is provided to exhibit the utilizability of the obtained control technique. [ABSTRACT FROM AUTHOR]

Summary: In this paper, the problem of adaptive security control for a class of nonlinear multi‐agent systems (MASs) under deception attacks is studied. This scheme aims to design a new adaptive fuzzy control scheme for each subsystem of MASs to reduce the impact of network attacks (from sensor to controller) on state signals, and proposes Nussbaum function to deal with unknown time‐varying control directions. In addition, for multi‐agent systems, a finite‐time convergent security control scheme is designed to ensure the convergence of tracking error and the boundedness of signals in the closed‐loop system. Finally, the effectiveness of the proposed method is verified by a simulation example. [ABSTRACT FROM AUTHOR]

Summary: With the increase in the penetration level of renewables into the grid, the virtual synchronous generator (VSG) concept has emerged as an attractive solution for controlling grid‐tied inverters. The variation in reactive power also affects the real power of the system as well. To address the voltage variations and phase angle variations occurring because of the energy storage elements, the control of the reactive power loop (RPL) is important. Also, it is used to maintain voltage regulation and improve the power factor of the VSG system. This paper focuses on the damping of oscillations occurring because of load variation by proper adjustment of the rotational inertia, damping coefficient, droop coefficient, and regulatory coefficient of reactive power with a proposed adaptive control strategy. Small‐signal analysis of the active and reactive power and point of common coupling (PCC) have been derived, and the detailed dynamic performances are analyzed. The influences of parameters perturbation on PCC voltage and reactive power were investigated. Furthermore, adaptive control includes optimizing dynamic indexes such as rise time, delay time, and response settling time. Finally, the performance and improvement in the results of VSG with and without adaptive control are compared. [ABSTRACT FROM AUTHOR]

Summary: In order to achieve efficiency extremum of power conversion independent of parameter values and operational mode, an adaptive dual‐controller on‐line efficiency optimization method is presented in this paper. Firstly, based on tri‐modal analysis for four‐switch buck‐boost (FSBB) converter, efficiency extremum analysis is given and existed for such this converter. Then, adaptive dual controller on‐line efficiency seeking algorithm is shown to adaptive two variables for efficiency optimization and output voltage regulation. Furthermore, perturb and observe detector is described to realize control variables decoupling of dual‐controller and to prevent the instability of the whole system during the algorithm tuning process and to improve the algorithm speed of the dual controller. The proposed method is model independent and does not depend on the circuit parameter values. The detail implement of algorithm and performance analysis is given, and the experimental platform and experimental results are put forward to verify the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Identification and controlling of non‐linear and complex dynamical systems have been a major concern and a serious topic of study in the field of adaptive control systems. Various techniques based on artificial intelligence have been used for this purpose. For modelling and control of plants, it is essential to optimize the values of the parameters associated with the respective technique. Searching the right or optimum value of the associated parameters is known as optimization. The intelligent water drop (IWD) algorithm is an optimization algorithm derived from the natural intelligence of water drop, which always chooses the shortest possible path while travelling from rivers and lakes towards oceans and seas. This paper introduces a new way of visualizing and understanding the IWD algorithm and presents its respective application in the field of modelling and controlling of non‐linear dynamic systems with the help of illustrative examples. Fuzzy type‐1 and recurrent fuzzy systems have been optimized using this algorithm. Its learning ability is compared with that of the grasshopper algorithm, a particle swarm optimization (PSO) based algorithm and the gradient descent method. The examples show the superiority of the IWD algorithm over the other optimization techniques. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel adaptive friction estimator for simple mechanical systems with Coulomb friction. We assume that the friction constant is unknown and we propose an adaptive observer for its estimation. We show that under certain conditions the observer structure is asymptotically stable. The proposed observer dynamics has two tuning parameters and essentially behaves as a second-order switching system. These parameters could be utilised to tune some time domain performance measures such as settling time, per cent overshoot. etc., under certain conditions. Lastly, simulations reveal that when the actual friction is not confined to Coulomb friction only, the proposed design improves the position tracking performance, especially when the position controller has low bandwidth. [ABSTRACT FROM AUTHOR]

In this article, the distributed adaptive finite-time containment control problem is investigated for nonlinear multi-agent systems with unknown input saturations. Since the leaders are only available to partial subsystems, a distributed estimator is designed to estimate the local reference signals. The saturated function is converted to the combination of a linear part and a nonlinear part, thus overcomes the difficulties caused by unknown saturated bounds. By utilising the command filtered and backstepping control techniques, a two-step method is adopted to construct the controller, under which the effect of unknown input saturations can be compensated well. It is proved that the closed-loop system is practical finite-time stable, and the output of each follower can converge to the dynamic convex hull spanned by the leaders in finite time. Finally, a group of four one-link manipulator systems is used to support the feasibility of the developed control scheme. [ABSTRACT FROM AUTHOR]

This paper investigates the finite-time time-varying formation-containment control problem for satellite formation flying under discontinuous control protocols based on nonsmooth analysis. The desired consequence is that the follower satellites can converge into a dynamic convex hull in finite time, which is spanned by leader satellites' time-varying formation according to the reference signal. Firstly, to accomplish this objective, a finite-time time-varying formation-containment control strategy is proposed for satellite formation system in present of the bounded disturbances and satellites' accelerations, which are known for each satellite. Then, considering the case that the information of disturbances may not be measured accurately and the accelerations of satellites may not be obtained globally in some practice application scenarios, a fully distributed adaptive control strategy is proposed by introducing the dynamic variables. The adaptive variables can compensate the unknown items and adjust with the evolution of neighbour satellites' states, which makes the controller more flexible. Moreover, the proposed control strategies are able to guarantee a fast dynamic response and also a suppression of disturbance. Finally, the performances of the proposed strategies are demonstrated by numerical simulations. [ABSTRACT FROM AUTHOR]

Brain tumor is an anomalous growth of glial and neural cells and is considered as one of the primary causes of death worldwide. Therefore, it is essential to identify the tumor as soon as possible for reducing the mortality rate throughout the world. However, the classification of brain tumor is a challenging task due to presence of irrelevant features that cause misclassification during detection. In this research, the adaptive manta ray foraging optimization (AMRFO) is proposed for performing an effective feature selection to avoid the problem of overfitting while performing the classification. The adaptive control parameter strategy is incorporated in the AMRFO for enhancing the search process while selecting the feature subset. The linear intensity distribution information and regularization parameter-based intuitionistic fuzzy C-means algorithm namely LRIFCM is used to perform the segmentation of tumor regions. Next, LeeNET, gray-level co-occurrence matrix, local ternary pattern, histogram of gradients, and shape features are used to extract essential features from the segmented regions. Further, the attention-based long short-term memory (ALSTM) is used to classify the brain tumor types according to the features selected by AMRFO. The datasets utilized in this research study for the evaluation of AMRFO-ALSTM method are BRATS 2017, BRATS 2018, and Figshare brain datasets. Segmentation and classification are the two different evaluations examined for the AMRFO-ALSTM. The structural similarity index measure, Jaccard, dice, accuracy, and sensitivity are utilized during segmentation evaluation, while accuracy, specificity, sensitivity, precision, and F1-score are used during classification evaluation. The existing researches namely, transformer-enhanced convolutional neural network, Chan Vese (CV)-support vector machine, CV-K-nearest neighbor, deep convolutional neural network (DCNN), and salp water optimization with deep belief network are used to compare with the AMRFO-ALSTM. The accuracy of AMRFO-ALSTM for Figshare brain dataset is 99.80 which is a greater achievement when compared to the DCNN. [ABSTRACT FROM AUTHOR]

This paper presents a new optimization approach for solving the under-frequency load shedding (UFLS) problem in power systems. UFLS is a very important function in maintaining the power system within its safe operating limits.It is also the last resort in the event of frequency instability. This paper investigates the use of an adaptive neuro-fuzzy inference system (ANFIS)-based controller. The proposed ANFIS provides optimal response compared to conventional load shedding and other methods. The proposed approach is based on the simulation of the multi-zone load frequency control problem. Rate of change of frequency, voltage deviation and tie-line power transit are used as inputs to the ANFIS. The outputs in the first level are the optimal frequency threshold (Fth) values, and in the second level, they are the quantity of load shedding. Numerical results based on the IEEE 39 power system and the Algerian grid are used to demonstrate the effectiveness of the proposed approach in finding the optimal UFLS control compared with the conventional method as well as the results of the other intelligent method. The comparison showed that the proposed scheme gave the best response in all scenarios. [ABSTRACT FROM AUTHOR]

In this article, an event-triggered finite-time neural control strategy is proposed for nonlinear power systems with unknown disturbances and static var compensator (SVC). We first transform the power system with SVC into a three-dimensional uncertain nonlinear system and then extend it to an n -dimensional uncertain nonlinear system. The disturbance observer is established to estimate external disturbances and the unknown nonlinear terms are approximated by the radial basis function neural networks. Moreover, to avoid the complexity explosion problem in the traditional backstepping method, the command filtering technique is adopted, and the error caused by the command filters is compensated. The adaptive event-triggered finite-time controller ensures that all signals are bounded in finite time and excludes Zeno phenomena. In the end, the simulation for the two-area interconnected power system with SVC is presented to verify the availability and feasibility of the proposed approach. [ABSTRACT FROM AUTHOR]

An adaptive control law encompasses a regulating control rule compensating for system dynamics variations by adjusting the controller characteristics to maintain the overall system performance. Recently, some techniques have been developed based on fundamental aspects of the adaptability of living organisms. The adaptive control method is a technique that measures the dynamic characteristics of the plant automatically and continuously to make a comparison with its required output. It utilizes the difference between these two to commute adaptable system parameters to maintain optimal performance regardless of the system variations. The behavior of the adaptation rule is significantly affected by the adaptation gain value. Here, it has also been investigated that the adaptation gain range is wide for the systems with the lower order. The appropriate range of adaptation gain decreases as the order of the system increases. In the present work, the model reference adaptive control (MRAC) for first- and second-order systems has been designed and investigated using a wide range of values for the adaptation gain and variations in the reference model parameters. The MIT (Massachusetts Institute of Technology) and Lyapunov rules are applied for the analyses of systems. On the MATLAB/Simulink platform, all the adaptation process comparisons, variations, and investigations have been carried out by altering the adaptation gain and the reference model parameters. The obtained results present encouraging outcomes. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive controller for a class of uncertain nonlinear systems is presented using a combination of Legendre polynomials and brain emotional learning-based intelligent controller (BELBIC). Recently, some versions of BELBIC have been presented with the aim of satisfying the universal approximation property using Gaussian basis function. However, the size of regressor vector is too large that imposes a heavy computational load to the processor. The novelty of this paper is presenting a new version of BELBIC with less computational burden using Legendre polynomials. Moreover, there are very few tuning parameters in Legendre polynomials. Another contribution of this paper is editing the stability analysis presented in recent related works. Due to the intrinsic non-differentiability of the adaptation rules of BELBIC, the second time derivative of Lyapunov function is undefined and thus, the Barbalat's lemma cannot be applied to verify the asymptotic convergence of the error function. Therefore, bounded-input-bounded-output (BIBO) stability can only be claimed for this controller. Simulation results on different case studies show that Legendre polynomials can improve the universal approximation property of BELBIC with less tuning parameters. Moreover, in the absence of the robust control term in the control law, the performance Legendre polynomials will not deteriorate, while the performance degrade in Gaussian basis function is quite considerable. [ABSTRACT FROM AUTHOR]

A current challenge with adaptive controllers is to define efficient tuning methods of the controller parameters. Unlike linear systems, nonlinear systems may need parameters that are continuously tuned at different operating points to provide stability and desired behaviours. This study aims to develop a solution for tuning proportional–integral–derivative (PID) controller parameters as opposed to changing the operating points of a nonlinear system. Most tuning methods calculate parameters according to the system's step or frequency response. However, adaptive controllers have self-tuneable parameters or control rules. The proposed algorithm in this paper contains a controller, an estimator, and a reference model, and uses the system model. Unlike the model reference adaptive control method, the proposed controller has tuneable controller parameters estimated by the extended Kalman–Bucy filter. The filter estimates the controller parameters to make the system perform like the auxiliary ideal reference model to ensure minimum-time consumption. Hence, this study aims to develop an algorithm that will automatically calculate controller parameters for each operating point of the controlled chaotic or nonlinear system to minimize settling time at each operating point. The proposed algorithm is implemented in a unified chaotic system in which the estimator and controller of the system run together. Simulation results confirm the performance of the proposed algorithm. In addition, the simulation results provide strong evidence that the proposed algorithm can be an effective tool for controlling nonlinear or chaotic systems. [ABSTRACT FROM AUTHOR]

Unknown actuator failures are inevitable in practical systems. At the same time, time delay exists in many physical actuators, and the system performance will be affected by such actuator delay and faults. However, the results of studies that attempt to compensate for unknown failures of actuators with time delay are still very limited. In this paper, such a problem is studied, and an adaptive control scheme is proposed based on backstepping approaches. First, the input delay of actuator faults and output disturbances are transformed into unknown effects on the output signal. In the backstepping recursive design, these unknown effects will accumulate to the last step of the controller design. Then, a new Lyapunov function is constructed by introducing auxiliary signals to prove the stability of the system. It is shown that the proposed control scheme can compensate for the effects caused by unknown actuator failures and input delays. The stability of the closed-loop system can be guaranteed by this adaptive controller. Finally, simulation studies are used to verify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper provides a new sliding mode control (SMC) approach, by which both the nominal and robust stability associated with a trajectory tracking problem for an uncertain robot manipulator are achieved. More precisely, the new control law consists of linear and nonlinear functions of tracking errors, in which the former is for the nominal stability and the latter is to ensure the robust stability of the resulting closed‐loop systems. The nonlinear function can be interpreted as an extended version of conventional SMC approach, and the reaching phase corresponding to a pregiven sliding surface is shown to be completed in a finite time; the tracking errors arrive at the sliding surface in a finite time and do not deviate from it after the arrival. In the sliding phase, the tracking errors are also ensured to converge to the origin. Finally, some simulation results are given to demonstrate both the theoretical validity and practical effectiveness of the proposed control approach. [ABSTRACT FROM AUTHOR]

Effective monitoring of the control charts requires the establishment of appropriate control limits. Various methods have been proposed to determine the fixed control limits (FCLs) based on a specified in‐control average run‐length value, such as Markov chains, integral equations, and Monte Carlo simulations.With the FCLs, the conditional false alarm rate (CFAR) of a control chart varies over time in an unexpected and undesirable way, where the CFAR refers to the probability of a false alarm at a particular time given no previous false alarm. To address this issue, dynamic probability control limits (DPCLs) can be employed to keep the CFAR constant over time. In this study, we determine the DPCLs using fixed and time‐varying sample sizes for the adaptive multivariate EWMA chart when the underlying process is assumed to follow a multivariate normal distribution. The DPCLs are designed to automatically adjust to changes in the probability distribution of the sample size, while maintaining a consistent CFAR. This results in a closely matched run‐length performance for an in‐control process compared to that of the geometric distribution. [ABSTRACT FROM AUTHOR]

Aiming at the poor transient convergence and real-time performance of the horizontal vibration state variables of the high-speed elevator car system, and the low control accuracy and stability, this paper proposes an adaptive control strategy to ensure the transient response of the elevator car system. First, a prescribed preset transient performance function is introduced into the controller design to control the variation range of the state variables and ensure the steady transient performance of the elevator car; Second, representing the information of observation error/control error through algebraic operations, designing an adaptive law based on e-correction, estimating unknown parameters in the elevator car system, and achieving online parameter updates; Then, using neural networks to learn and compensate for unknown dynamics in the elevator car system, and solving the online estimation problem of neural network weights through adaptive laws, so that the tracking error and weight estimation error converge to a tight set near zero; Finally, using MATLAB/SIMULINK to compare and analyze the four control algorithms of passive control, PID control, adaptive control based on gradient descent method and transient response adaptive control proposed in this paper under two different rail excitations: Random excitation and pulse excitation. The simulation results show that the adaptive control strategy proposed in this paper effectively suppresses the horizontal vibration of the elevator car, makes the state variables have faster convergence speed and smaller convergence error, and ensures the stable and transient performance of the elevator car system. [ABSTRACT FROM AUTHOR]

This paper proposes a novel incipient fault estimation and fault-tolerant control approach for wireless power transfer (WPT) systems with disturbances and incipient sensor faults. Firstly, the dynamic models of the WPT system and incipient faults are established. Then, a k-step incipient fault estimation observer method is analyzed for estimating the states and incipient faults of the system. Based on it, a nonlinear dynamic output feedback fault-tolerant controller is devised to ensure the stability of the WPT system when considering incipient faults and disturbances. Further, the designed controller can monitor the system in real time without the knowledge of the system states. Besides, the stability analysis approach is rigorous, and the proof method can also apply to other similar systems. At last, the simulation example shows that the proposed fault diagnosis method is correct and effective. [ABSTRACT FROM AUTHOR]

In this article, the adaptive finite time prescribed performance tracking control problem of a class of high‐order uncertain nonlinear systems under full state time‐varying constraints is addressed. By using the logarithmic function based nonlinear mapping technique rather than the barrier Lyapunov function based method, the obstacle of the full‐state constraints is resolved, and the undesired "feasibility conditions" in controller design are eliminated. By improving the prescribed performance control method, the initial value problem is overcome, and the finite‐time convergence of the tracking error can be ensured. Then, with the aid of the adding a power integrator method, an adaptive finite time prescribed performance tracking controller is designed, not only the tracking error but also the full states can be guaranteed to converge to a predefined constraint region in the predetermined time. Simulation examples are given to show the validness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

In this article an optimal control scheme is proposed to solve robust control problem for matched and unmatched system. In the proposed optimal approach the value functions are designed such that the obtained optimal control law guarantees asymptotic stability of the uncertain nonlinear system. Since the proposed robust optimal control problem is not straightforward to solve, an off‐policy reinforcement‐learning algorithm based on neural networks approximation is developed to obtain robust optimal control law iteratively. The robust control law for matched uncertain systems can be achieved via proposed off‐policy learning algorithm without requiring exact knowledge of system's dynamics. The advantages of the proposed robust optimal controller are verified by comparative simulations on an uncertain model of a car suspension system and a mathematical nonlinear model. [ABSTRACT FROM AUTHOR]

This article proposes a novel multidimensional Taylor network (MTN)‐based adaptive finite‐time control approach for stochastic nonlinear systems with input saturation constraints. First, by introducing the hyperbolic tangent function, a piecewise smooth function is proposed to approximate the input saturation constraints, which can solve the problem caused by input saturation. Then, with the aid of MTN approximation theorem, a novel adaptive finite‐time control approach is proposed, which can guarantee that all signals of the closed‐loop system are semi‐globally finite‐time stability in probability (SGFTSP), the system output follows the given reference signal, meanwhile, the tracking error converges to a small neighborhood of the origin in finite‐time. Compared with the existing results, the proposed approach has the characteristics of simple structure and low calculation, which can achieve the satisfactory control performance with a reduced computational burden. In the end, the effectiveness of the proposed approach is validated by Lyapunov stability theory and two simulation experiments. [ABSTRACT FROM AUTHOR]