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This paper addresses the stochastic communication protocol (SCP) based controller design problem for discrete delayed system with stochastic uncertainty and Markovian packet dropouts. This issue is frequently occurred in networked control system where the packet dropouts bring obstacle for controller design. Especially, the controller terms are not allowed to be transformed directly into actuator side under the SCP. Thus, the traditional approaches are not effective to solve this design issue. In this paper, a novel sliding mode control (SMC) mechanism is given to handle the influences caused by packet dropouts, time-varying delay and SCP. A protocol-based controller is designed to guarantee that the closed-loop system is mean-square asymptotically stable with satisfactory H ∞ performance. Moreover, an optimization algorithm is further provided to compute the protocol-based gain matrix. Finally, a simulation example is proposed to verify the effectiveness of the presented protocol-based SMC design procedure. [ABSTRACT FROM AUTHOR]

This paper proposed an Immersion and Invariance (I&I) adaptive fault-tolerant safe control scheme for a class of safety-critical (SC) systems with parametric faults. The scheme combines I&I Adaptive Control Lyapunov Function (I&I aCLF) with I&I Adaptive Control Barrier Function (I&I aCBF) to shape the transient performance of constrained states. Forward invariance of a safe set is achieved through online parameter adaptation, and the parameter convergence is guaranteed through I&I adaptation law. The conservatism is reduced by both shaping transient performance and ensuring parameters convergence. A SC controller guaranteeing stability and safety is obtained by solving an optimization problem with safety constrain, and an analytical solution is provided to facilitate the calculation and execution of the SC controller. A numerical simulation is carried out to illustrate the effectiveness of the proposed method. • This paper presents I&I aCLF and I&I aCBF, which are used to design controllers for ensuring the stability and safety of nonlinear systems with parametric faults. • The proposed controller provides less conservatism and improves the transient performance of constrained states. The parameter convergence is guaranteed by the proposed adaptation law. • To facilitate the calculation and execution of the controller, an analytical controller for a class of SC systems with parametric faults is given. [ABSTRACT FROM AUTHOR]

This paper investigates the adaptive fixed-time tracking control problem for a class of stochastic nonlinear systems with constrained states and input saturation. By utilizing a barrier Lyapunov function (BLF), the issue caused by constrained states is settled. Simultaneously, a smooth function is constructed to approximate input saturation and the unknown nonlinear function is approximated by neural networks (NNs). In view of the fixed-time tracking control problem of stochastic nonlinear systems, a fixed-time stability lemma for stochastic nonlinear systems has been established and proved. Under the designed controller, the closed-loop system is semi-globally fixed-time stable in probability (SGFSP), and all the states are constrained within a compact set. Moreover, the controlled system in this paper achieves equilibrium within a fixed-time internal. Lastly, the presented method is verified by a simulation example. [ABSTRACT FROM AUTHOR]

In this paper, the finite-time control and bumpless transfer control are investigated for switched systems under asynchronously switching. First, a class of dynamic output feedback controllers are designed to stabilize the switched system with measurable system outputs. Considering the improvement of transient performance, the bumpless transfer control and finite-time control are further studied in the controller design. To avoid the control bumps, a practical filter is introduced to make the control signal smoother and continuous. Furthermore, to derive a finite-time bounded system state over short-time intervals, the finite-time analysis is considered in managing the switching process with the average dwell time. New criteria are proposed to analyze the finite-time stability and finite-time boundedness for the closed-loop system and solvable conditions are newly proposed to optimize the controller gain. Finally, the superiorities of the proposed method are validated through an application to a boost converter. [ABSTRACT FROM AUTHOR]

This paper investigates the design of fault-tolerant adaptive event-triggered integral sliding mode control (FTAETISMC) for a class of uncertain systems in the presence of partial actuator failures. The proposed control scheme guarantees the closed-loop system to operate properly in case of partial actuator failures and ensures the reachability of the sliding mode surface. In order to reduce the consumption of system resources, an adaptive event-triggered (ET) mechanism is introduced, where the threshold is based on a function of a non-negative adaptive parameter. By choosing the parameter of the integral sliding mode manifold, the asymptotic stability of the system can be achieved. To ensure that the system does not suffer from Zeno phenomenon, there exists a positive lower bound of the inter-event time. Finally, simulation results of a numerical example are presented to prove the benefits of the proposed method. • A networked control systems with matched and unmatched uncertainties under actuator failures is discussed. • A robust controller with integral sliding mode can eliminate uncertainties. • An adaptive event-triggered mechanism is proposed. • A minimum positive lower bound on the inter-event time to avoid Zeno phenomenon is obtained. [ABSTRACT FROM AUTHOR]

This paper investigates the resilient leader-following consensus problem for a class of high-order multi-agent systems subject to unmatched lower triangular uncertainty dynamics and sensor–actuator attacks. Compared with previous work, the sensor and actuator attacks are modeled to be both time-varying and state-dependent, which makes the resilient consensus control more challenging. To deal with the unmatched uncertainties and sensor–actuator attacks, a new compensator with two online tuned dynamic gains is proposed relying only on the relative output measurements of neighboring agents. Then, using only the local compensation information, a fully distributed output feedback resilient protocol is designed to guarantee the leader-following consensus. Sufficient conditions in terms of matrix inequalities are derived to determine control gains and compensator parameters and simulation studies are presented to demonstrate the proposed theoretical results. [ABSTRACT FROM AUTHOR]

This paper introduces the signed graph into fractional-order coupled neural networks (FCNNs) and the bipartite synchronization is investigated for leaderless FCNNs. Instead of formulating leader's state or isolated node's state as the synchronization reference target, the bipartite synchronization of leaderless FCNNs is discussed by developing a direct error approach. First, an important fractional-order inequality is rigorously proved by contradiction. By virtue of fractional-order inequality, gauge transformation and several analytical tools, the criteria of bipartite leaderless synchronization are obtained for FCNNs with heterogeneous and homogeneous coupling weights. Specially, for the two types coupling weights, the adaptive pinning schemes are adopted which just rely on partial network information based on the spanning tree and connected dominating set, respectively. Eventually, the theoretical analysis is verified by two numerical simulations. [ABSTRACT FROM AUTHOR]

Radial Basis Function Neural Networks (RBFNN) methods have gained incredible efficiency and applicability in control. This paper presents a nested control strategy for robust trajectory tracking of a quadrotor using adaptive RBF compensation and NN-supervised control embedded with Integrator BackStepping (IBS). The approach addresses the robustness in the presence of modeling uncertainties, sensing noise, and bounded disturbances. The control design is derived from the decentralized inverse dynamics, using adaptive RBFNN for outer-loop disturbance approximation and compensation. In conjunction with an Inner-loop supervised control that stabilizes the quadrotor attitude, preventing initial instability during NN convergence. In addition, an adaptive Extended Kalman Filter (EKF) attenuates noisy signals. Simulation results demonstrate strong adaptability to changes in model parameters, and superior performance when compared to Proportional Integral Derivative (PID), Integrator BackStepping (IBS), and offline decentralized Multi-Layer Perceptron (MLP) algorithms, in terms of parameter convergence, disturbance compensation control, and noise attenuation. [ABSTRACT FROM AUTHOR]

In this paper, we propose a fault diagnosis (FD) approach for a class of nonlinear uncertain systems based on the deterministic learning approach (DLA). Specifically, an adaptive learning observer is constructed, in which the adaptive neural networks (NNs) are constructed to approximate the unknown system dynamics under normal and fault modes. Based on the strictly positive real (SPR) condition, the convergence of the state estimation can be guaranteed. When the system is undergoing a periodic or periodic-like (recurrent) motion, the states of the observer will also become recurrent. Thus through DLA, the partial persistent excitation (PE) condition of the associated subvectors of NNs is satisfied. By utilizing the partial (PE) condition, the uniformly completely observable (UCO) property of the identification system is analyzed and the exponential convergence condition of the identification system is derived. Under this condition, the unknown dynamics under normal and fault modes can be accurately identified along the system trajectory. And by utilizing the knowledge obtained in the identification phase, the fault can be detected in the diagnosis phase. The main attraction of this paper lies in the analytical result, which shows that the exponential convergence condition of the learning observer not only depends on the observer gain matrix, but also depends on the PE level of the regressor subvector of NN. Simulation results are included to illustrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper investigates the optimal backstepping control strategy for strict-feedback nonlinear systems subject to input saturation and time-varying partial output constraints. Compared with the existing results on output constraints, the time-varying partial output constraint is a more general constraint that can start and end at any time during the system operation, or remain unconstrained, which can be called as output constraints occurring in a limited time interval (OCOLT). A special barrier function is embedded into the optimal performance index function to satisfy the OCOLT condition under the optimal control framework, and a shift function is introduced to address the function discontinuity caused by the OCOLT problem. Then, an auxiliary system and a disturbance observer are respectively constructed to handle the influence of the input saturation and compounded disturbances. Meanwhile, the simplified reinforcement learning algorithm, employing the identifier-critic-actor architecture, is developed to achieve the optimal control objective within the framework of the backstepping technology. Moreover, the proposed optimal control method ensures the semi-globally uniformly ultimately bounded of all signals within the closed-loop system. Finally, two simulation examples are given to further prove the effectiveness of the presented optimal control approach. [ABSTRACT FROM AUTHOR]

The paper presents a robust adaptive control strategy to attain consensus of a specific group of nonlinear and disturbed multi-agent systems during a predefined-time. To address the impacts of nonlinear dynamics and disturbances, adaptive schemes are developed to assess the state-dependent rates and constant boundaries of disturbances and nonlinearities immediately. Relying on the adaptive updated information, a disturbance rejection consensus controller is proposed to synchronize consensus errors within the defined error model, so that the errors can tend to zero within predefined time impacted by nonlinearities and disturbances. The efficacy and validity of the proposed approach is substantiated by the comparative simulation results obtained from a system comprising Chua's circuits. [ABSTRACT FROM AUTHOR]

A fixed-time controller based on improved fixed-time predictor line-of-sight (FxTPLOS) guidance and a nonsymmetrical prescribed performance constrained controller is designed for the path-following of underactuated marine surface vehicles (MSVs) in the presence of unknown external disturbances and uncertainties. First, a fixed-time error prediction model is established, and an internal model-controlled observer is designed to observe the sideslip angle, which can extend the estimated range of the sideslip angle to any range. Second, a nonlogarithmic performance function is introduced that can converge the error to a prescribed range, and a fixed-time controller is designed by error transformation. A new input saturation function is added to solve the problem of input saturation, and a new filter is added to avoid differentiating the virtual control law. Third, a novel fixed-time lumped disturbance observer is proposed for estimating the model uncertainty and external disturbances. It is possible to achieve accurate disturbance compensation. By using Lyapunov theory to analyze the stability of the closed-loop control system, it is proven that the system can be uniformly globally fixed-time stable (UGFxTS). Finally, simulation experiments prove that the control strategy proposed in this paper is effective. [ABSTRACT FROM AUTHOR]

This paper addresses a fault-tolerant formation tracking problem for multiple mobile robots, where the dynamics of each robot is nonholonomic with time-varying input delays and actuator faults. The control problem is challenging since the actuator faults (e.g., loss of effectiveness and biased faults) are unknown in the time-delay multi-agent systems. To this end, an adaptive compensator is proposed for the uncertain time-delay systems induced by the loss of effectiveness faults. With the aid of neural networks and auxiliary variables, a novel adaptive fault-tolerant formation tracking controller for each robot is developed without any restrictions on the rate of their unknown variables induced by the biased faults. The uniformly ultimately boundness for the closed-loop system is derived according to the Lyapunov–Krasovskii functional. Numerical simulations verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a class of interconnected systems is considered, where the nominal isolated subsystems are fully nonlinear and non-minimum phase. A decentralized Extended Kalman Filter-Extended High Gain Observer (EKF-EHGO) is designed to observe the system states. Then, a systematic backstepping design procedure is employed to develop a novel decentralized robust adaptive output feedback control, in which the adaptive law is designed to counter the effects of the interconnections and uncertainties. The proposed decentralized dynamic output feedback control scheme can guarantee that all the signals in the closed-loop system are uniformly ultimately bounded (UUB). Both interconnections and uncertainties are allowed to be unmatched and bounded by an unknown high-order polynomial, which is a more general form when compared with existing work. Two MATLAB simulation examples are used to demonstrate the effectiveness of the proposed method including a system comprising translational oscillator with rotating actuator (TORA) sub-systems. [ABSTRACT FROM AUTHOR]

This paper is concerned with distributed fixed-time containment control for nonlinear multiagent systems with actuator faults via static and dynamic event-triggered strategies. A distributed fixed-time static event-triggered fault-tolerant controller is developed by employing the sign function, which eliminates the requirement for continuous control signal updates. To address the chattering phenomenon caused by the sign function, a distributed saturation controller is presented to enhance the control performance. To further reduce energy consumption and frequency of controller updates, a dynamic event-triggered strategy with an auxiliary parameter is proposed for each agent to dynamically regulate its threshold. The control scheme guarantees that the containment control objective is achieved within the settling time, and does not exhibit Zeno behavior. The effectiveness of the proposed control scheme is illustrated by simulation results. [ABSTRACT FROM AUTHOR]

This paper studies the trajectory tracking control problem of uncertain manipulators with full-state constraints and dead-zone inputs. By utilizing the backstepping method and barrier Lyapunov functions, a novel disturbance observer-based full-state constrained control approach is proposed. It is noted that the dead-zone inputs can be expressed in terms of a nonlinear disturbance term and a nominal part. The unknown dynamic uncertainty of robotic system is approximated by the adaptive neural network, and the disturbance observer is integrated into the control design to compensate neural network approximation errors, nonlinear disturbances caused by dead-zone inputs, and external disturbance. A barrier Lyapunov function (BLF) is introduced to illustrate the boundedness of signals within the robotic control system. Finally, the effectiveness of the proposed control scheme is illustrated through simulation results of a two-link manipulator. [ABSTRACT FROM AUTHOR]

This paper investigates the finite-time (FT) adaptive output feedback control for a class of nonlinear systems with asymmetric state-function constraints and unknown external disturbances. Incorporating the asymmetric barrier Lyapunov function (BLF) into the backstepping technique, a new control approach is proposed to ensure that all states do not cross the constraint boundary. With the help of the command filter, the explosion of complexity induced by repeated differentiations of virtual controllers is eliminated, and the corresponding compensation system is used to offset the filter error. Meanwhile, the fuzzy logic system (FLS) and fuzzy state observer are employed to approximate unknown nonlinear functions and estimate unmeasurable states respectively. For effectively conserve communication resources, a novel adaptive event-triggered controller is proposed such that all signals of the closed-loop system are bounded, the tracking error converges to a small neighborhood containing the origin in finite time. Finally, the feasibility of the fabricated control scheme is verified with two examples. [ABSTRACT FROM AUTHOR]

This paper presents a fixed-time adaptive resilient control framework based on reinforcement learning (RL), mismatch disturbance observer and nonsingular fast terminal sliding mode scheme for nonlinear cyber-physical systems (CPS) to enhance cyber-security under False Data Injection (FDI) attacks, as well as match and mismatch disturbances. The RL algorithm contains an actor-critic neural network (NN) in which the actor NN is employed to estimate the uncertainty while the critic NN is applied to evaluate the performance cost function. Next, to compensate the detrimental effects of attacks and mismatch disturbances, a new sliding mode-based adaptive disturbance observer is designed. To achieve high precision trajectory tracking within a fixed-time interval, a nonsingular fast terminal sliding mode scheme is designed. This scheme ensures fixed-time convergence of the tracking error and facilitates disturbance attenuation and attack mitigation which are the key features of the proposed fixed-time secure control strategy. Finally, the fixed-time stability analysis of is performed through Lyapunov theory. Results reveal the effectiveness of the proposed resilient control for wind turbine system as the nonlinear CPS. [ABSTRACT FROM AUTHOR]

This paper explores the finite-time boundedness and controller problem of switching nonlinear delay systems. The time delay is considered as time-varying delay rather than constant delay. First of all, an appropriate Lyapunov–Krasovskii functional (LKF) which contains three integral terms is selected. The upper and lower bounds of these integral terms are given by segmenting the time delay interval. Meanwhile, the auxiliary matrix method is used to separate the Lyapunov matrix from the system matrix. Moreover, a new state-dependent switching law is designed to switch between subsystems according to the change of the magnitude of the LKF. Based on the above methods, the first step is to research the finite-time bounded analysis of delayed system without control input. Then, a sufficient condition is obtained to ensure the finite-time boundedness of switched systems with control input. Further, by designing an observer-based H ∞ controller, the closed-loop system with time-varying delay is bounded and has H ∞ performance index ν by using the Finsler's Lemma and Singular Value Decomposition (SVD). Simultaneously, the controller and observer gains are designed. Finally, two simulations are used to verify the effectiveness and feasibility of the proposed methods. • Investigating the finite-time boundedness and H ∞ control for switching nonlinear delay systems. • The time delay is considered as time-varying delay rather than constant delay. • Three integral terms are contained in the Lyapunov–Krasovskii functional. • The auxiliary matrix method is used to separate the Lyapunov matrix from the system matrix • A new state-dependent switching law is designed to switch between subsystems. [ABSTRACT FROM AUTHOR]

• A novel event-triggering mechanism and three dynamic gains are introduced to compensate uncertainty and execution error. • It overcomes difficulties from unknown polynomial growth rate, unknown large measurement sensitivity and time-delay. • It proves that all the signals are bounded, the states converge to zero asymptotically, and the Zeno behavior is excluded. In this paper, the problem of adaptive event-triggered output-feedback stabilization is addressed for a class of switched nonlinear systems with time-delay and output measurement sensitivity. With the assistance of the dynamic high gain technology and a novel event-triggered control mechanism, we overcome the design complexities arising from unknown polynomial growth rate with respect to the output, unknown large measurement sensitivity, unknown time-varying control coefficient and time-delay. Then, an effective constructive method is provided to design switched observers and a common event-triggered controller. Through introducing two key matrix inequalities and constructing a common Lyapunov-Krasovskii functional, sufficient conditions are given to get rid of Zeno behavior, and ensure that all the signals are bounded and the closed-loop system states converge to zero under arbitrary switchings. Two simulation studies are conducted for verifying the effectiveness of proposed scheme. [ABSTRACT FROM AUTHOR]

This paper discusses the event-triggered composite anti-bump H ∞ control problem for the networked switched systems in the finite-time stability sense. An event-triggered scheme (ETS) is constructed to reduce resource usage while maintaining desired control performance. Then, a novel switching bumpless control method based on ETS is given to resist the impact of bump transition between different modes. Sufficient criteria for finite-time boundedness of the closed-loop systems with bumpless transfer characteristics and H ∞ performance are derived by resorting to a mode-dependent average dwell time (MADT), and a trigger-based bumpless transfer controller is designed. Finally, a simulation example is provided to demonstrate the efficacy and feasibility of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper investigates the incipient fault-tolerant problem of heterogeneous multiagent systems and applies the proposed method to the wireless power transfer grid. Firstly, a novel extended state observer is designed for every follower system, which not only relative output information of its agent but also contains output information of neighboring agents. Then, a state estimation feedback control scheme is proposed to compensate for the time delay and incipient faults effectively. It is worth noting that the proposed observer-based control strategy distributes the weight of the output information error. Therefore, the observer has higher adjustability and more substantial applicability. Further, we make a consistent analysis of the system under the proposed control scheme with the Lyapunov–Krasovskii theory that the sufficient conditions can be formed for the stability of the system. Finally, the validity of the proposed control scheme is demonstrated through numerical and application examples. [ABSTRACT FROM AUTHOR]

This paper addresses the adaptive event-triggered (AET) stabilization problem for semi-Markovian jump systems (SMJSs) with deception attack. In the proposed AET control scheme, the threshold is adaptively updated unlike the existing event-triggered (ET) mechanisms, which can reduce communication pressure. By resorting to a mode-dependent Lyapunov functional, the stochastic stability and dissipative performance of SMJSs are guaranteed. In addition, according to stability conditions, the corresponding mode-dependent gain parameters are designed. At last, two numerical examples are given to demonstrate the effectiveness and applicability of the proposed theoretic results. [ABSTRACT FROM AUTHOR]

• A concurrent learning command filtering control scheme is designed for space manipulators with parameter uncertainties, external disturbances and input saturation to achieve the precise control of spacecraft attitude and end-of-arm trajectory simultaneously. • The fixed-time stability and easily adjustable convergence rate of estimated parameters are achieved, and the convergence rate is independent of the unpredictable excitation levels. • The designed fixed-time concurrent learning law is also robust to the non-conserved linear and angular momentum. • A fixed-time command filtering strategy with error compensation and saturation compensation is proposed to avoid the derivation operation of Jacobi/inertia matrices. This paper presents a concurrent learning command filtering control scheme for space manipulators in the post-capture phase. Due to the complex space environment and inherent model characteristics, space manipulators are subject to parameter uncertainties, non-conserved momentum and input saturation, which will degrade the control performance. To this end, a novel fixed-time concurrent learning law is developed in the presence of the non-conserved linear and angular momentum, where the convergence rate of the estimated parameters is determined by the control coefficients rather than the unpredictable excitation level. Then, a fixed-time command filtering strategy with the error compensation and saturation compensation is designed, to cope with input saturation and avoid the derivation operation of Jacobi matrices and inertial matrices simultaneously. By means of the proposed control scheme, the spacecraft attitude regulation error and the end-of-arm trajectory tracking error can converge to the small neighborhood of zeros within a fixed time. Simulation results demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

• A composite error transformation for addressing the initial condition dependence issue. • Cross-backstepping control with prescribed performance for underactuated systems. • The stability of closed-loop systems is rigorously analyzed. • Two typical numerical examples are conducted. [Display omitted] Underactuated robotic systems with input coupling are widely applied in practice. However, designing controllers for such systems poses significant challenges due to the complexities arising from input coupling. To address this issue, this paper proposes a novel cross-backstepping control strategy based on a composite error transformation. The transformation compresses the system's initial error into a definable range to deal with the issue of initial value dependency of prescribed performance control, while maintaining the transient and steady state of the system. Based on such a technique, a cross-backstepping controller is proposed whose core idea lies in constructing a virtual term that crosses with coupled states to generate a unified form, which serves as a bridge between multiple coupled states and a single actual control input. Furthermore, a nonlinear disturbance observer is introduced to improve the robustness of the system. The stability of the closed-loop system is proved according to the Lyapunov theorem, and two numerical examples of underactuated robotic systems with coupled inputs are presented to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In previous research on fault switching of redundant actuators using monitoring modules, the interference of sensor faults on monitoring results has not received enough attention. In this paper, a new fault-tolerant control (FTC) method combining adaptive compensation and active switching is proposed for a class of uncertain nonlinear systems with backup actuators against actuator and sensor faults. While using command filtered backstepping to reduce computational burden, a direct adaptive compensation scheme is designed to ensure that sensor faults within tolerable bounds do not trigger actuator switching. Based on this, a new lumped monitoring function (MF) containing prescribed performances and tolerable sensor fault bounds is designed to detect and isolate faulty actuators and ensure that the tracking error always satisfies prescribed transient and steady-state performances. Two simulation cases are used for illustrating the proposed method. It is proved that the proposed method can effectively reduce the interference of sensor faults on actuator fault detection and improve the accuracy of actuator switching. [ABSTRACT FROM AUTHOR]

This paper investigates an adaptive event-triggered control for semi-Markovian jump linear systems with process time-varying delays. For reducing the data transmission amount from the sensor-to-controller, a switching-mode dependent adaptive event-trigger (AET) is proposed wherein a positive auxiliary function is involved. Utilizing the received non-periodical state information, a switching state feedback control law is investigated. By aid of an exponential inequality technique, the controller design method is conveniently presented. The proposed control method not only guarantees the globally exponentially stability of the closed-loop system but also is capable of regulating the decay rate. An approximation approach for the minimal event-triggered time-interval of the AET is given as well. It has been proved that the unwanted Zeno behavior will not exhibit. The proposed control method is employed to a transmission control protocol based active queue management (TCP/AQM) system and the numerical experiment demonstrates its effectiveness. [ABSTRACT FROM AUTHOR]

It is difficult to apply the event-triggered adaptive dynamic programming (ET-ADP) algorithm to Itô-type stochastic systems due to stochastic disturbances. Therefore, few studies have addressed the ET-ADP algorithm for Itô-type stochastic systems. To fill this gap, this paper investigates a guaranteed cost fault-tolerant control problem for uncertain stochastic systems via a dynamic ET-ADP algorithm. First, the original control problem is transformed into the optimal control problem of a nominal system, and sufficient criteria are presented to verify the feasibility of the problem transformation. Second, a dynamic event-triggered condition is designed. Then, the optimal control of the nominal system is achieved via an ET-ADP algorithm. Finally, a numerical simulation and a comparative experiment are presented to illustrate the effectiveness of the developed control scheme. [ABSTRACT FROM AUTHOR]

This paper proposes a nonsmooth funnel function (NFF)-based model-free adaptive sliding-mode control strategy for the discrete-time nonlinear systems with unknown dynamics and asymmetric constraints. The dynamic linearization model of the nonlinear systems with unknown dynamics is obtained online only using the input/output data, which is combining with the designed NFF-based discrete-time sliding-mode surface to guarantee asymmetric constraints of the tracking error. In the proposed NFF, the coordinate transformation for asymmetric constraints simplifies the controller design, and the nonsmooth parameter can improve the transient and steady-state performances of the tracking error. The control consumption is further reduced by a proposed time-varying weighting factor-based cost function in the controller design. Furthermore, parameter selections of performance boundaries and the sliding-mode surface are given, and the performance margin (the minimum distance from the tracking error to the preset boundaries) is proved theoretically. Simulation and experimental results verify the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

The adaptive tracking problem for a class of strict-feedback switched nonlinear systems with output constraints, quantization input, distributed time-varying delays and external disturbances is studied in this paper. Firstly, we introduce the hyperbolic tangent function to handle the quantized signal in the design of the controller. For the nonlinear term and distributed time-varying delay of the system, the integral term is introduced into the candidate functional to compensate it. Secondly, based on Dynamic Surface Control (DSC) technique and Barrier Lyapunov function (BLF) method, an adaptive controller is designed to guarantee the stability of the controlled closed-loop system, and the system tracking error can converge to the origin without violating the constraints. Finally, simulation results verify the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper studies the problem of event-triggered adaptive control for a class of strictly-feedback stochastic nonlinear systems. The output dead zone and time-varying full-state constraints are considered in the controller. Firstly, time-varying barrier Lyapunov functions (TVBLFs) are introduced to address problems of time-varying state constraints. Then, the Nussbaum-type function is used to compensate for the influence of the output dead zone. The problem of state constraint is well solved when the system's output signal is affected by the dead zone, which satisfies a wider application space. In addition, a new relative threshold of the event-triggered control (ETC) scheme is considered to save communication resources, and the Zeno behavior is excluded. This scheme makes the trigger interval more flexible and optimizes the performance of the controller. Moreover, the error compensation signals reduce the filtered errors and simplify the algorithm. Simultaneously, an adaptive tracking controller with the backstepping technique is designed so that all signals in the closed-loop system are bounded and the tracking error converges to a small residual set of the origin. Finally, two simulation examples are given to demonstrate the effectiveness of the control method. [ABSTRACT FROM AUTHOR]

In this paper, a fault-compensation-based boundary control strategy is developed for a class of distributed parameter systems expressed by second-order hyperbolic Partial Differential Equations (PDEs), in the case of actuator faults. Considering multiplicative actuator faults and additive actuator faults simultaneously, we put forward a hierarchical fault compensation scheme, i.e., an adaptive iterative learning boundary control technique. Lyapunov-Like analysis method and a variation of Wirtinger's inequality are used to design a boundary control scheme with an adaptive law, guaranteeing the asymptotical stability of the closed-loop hyperbolic PDE system with multiplicative faults. An iterative learning term is proposed to boost the performance of the system under both multiplicative faults and additive faults, and a composite energy function is used in analysis. With the proposed fault-compensation-based adaptive iterative learning boundary control technique, multiplicative faults are redeemed towards the time horizon and additive faults are compensated towards the iteration horizon. An active acoustic noise reduction process is presented to illustrate the merit and effectiveness of the designed adaptive iterative learning boundary control technique. [ABSTRACT FROM AUTHOR]

This paper studies the fault-tolerant control (FTC) problem of a class of strict-feedback nonlinear systems. First, we put forward a key theorem which shows that type-B Nussbaum functions can be extended to the cases containing multiple Nussbaum functions in the same Lyapunov inequality under certain conditions. Then, by using the techniques of Nussbaum functions and adaptive control, a new fault-tolerant control scheme is proposed. Compared with the previous work, this paper considers unknown time-varying control coefficients and unknown time-varying fault coefficients of actuators. It is proved that all the signals of the closed-loop system are globally bounded and the tracking error converges to zero asymptotically. Finally, simulations are provided to verify the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

The stability problem of fractional-order neural networks in consideration of time-varying delay is addressed by utilizing Lyapunov functional approach in this paper. Firstly, a class of novel Lyapunov-Krasovskii functions are designed to deal with the time-varying delay terms, which reduces the conservatism of the stability criteria. In addition, to estimate the fractional-order derivative of the Lyapunov-Krasovskii functions, a novel free-matrix-based fractional-order integral inequality is then established, which encompasses the Wirtinger one and leads to tractable linear matrix inequality criteria. Then, based on the designed adaptive control and the proposed Lyapunov-Krasovskii functions, some stability criteria depending on time-varying delay information and fractional-order α are deduced. Finally, numerical simulation shows that the presented approach can significantly reduce the conservatism of the existing results and has a broader application prospect. [ABSTRACT FROM AUTHOR]

This paper investigates dynamic event-triggered adaptive leader-following semi-global bipartite consensus (SGBC) of multi-agent systems (MASs) with input saturation. A dynamic event-triggered adaptive control (DETAC) protocol is presented, where the triggering function can regulate its threshold value dynamically. It's turned out that the SGBC can be achieved via the DETAC protocol under some inequalities. Then, the proposed DETAC protocol is extended to solve bipartite consensus under jointly connected topology. Furthermore, the Zeno behaviors will be avoided. Finally, the rationality of proposed DETAC protocols are tested by simulation results. [ABSTRACT FROM AUTHOR]

In this paper, we present a unifying neural networks-based adaptive control framework for nonholonomic systems with partial/full state constraints or without constraint requirements. By considering the tan-type barrier Lyapunov function, constraint requirements are handled for the x 0 -subsystem. A novel nonlinear state-dependent function is constructed to meet asymmetric time-varying state constraints for the x -subsystem. Combined with backstepping method, a coordinate transformation is designed to transform the original constrained system into unconstrained system. Then, we put forward the dynamic surface control to eliminate feasibility conditions of virtual controllers. In addition, neural networks can be introduced to compensate for the uncertainties of nonholonomic systems. It is demonstrated that without modifying control structure the adaptive neural networks controller guarantees the stability of nonholonomic systems, while asymmetric time-varying state constraints can be satisfied. Simulation examples of mobile robot systems further validate the efficacy of the devised algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the optimal model reference adaptive control (MRAC) problem is studied for the unknown discrete-time nonlinear systems with input constraint under the premise of considering robustness to uncertainty. Through an input constraint auxiliary system, a new adaptive-critic-based MRAC algorithm is proposed to transform the above problem into the optimal regulation problem of the auxiliary error system with lumped uncertainty. In order to realize the chattering-free sliding model control for the auxiliary error system, an action-critic variable is introduced into the adaptive identification learning. In this case, the closed-loop control system is robust to the disturbance and the neural network approximation error. The uniformly ultimate bounded property is proved by the Lyapunov method, and the effectiveness of the algorithm is verified by a simulation example. [ABSTRACT FROM AUTHOR]

In this paper, a security consistent tracking control scheme with event-triggered strategy and sensor attacks is developed for a class of nonlinear multi-agent systems. For the sensor attacks on the system, a security measurement preselector and a state observer are introduced to combat the impact of the attacks and achieve secure state estimation. In addition, command filtering technology is introduced to overcome the "complexity explosion" caused by the use of the backstepping approach. Subsequently, a new dynamic event-triggered strategy is proposed, in which the triggering conditions are no longer constants but can be adjusted in real time according to the adaptive variables, so that the designed event-triggered mechanism has stronger online update ability. The measurement states are only transmitted through the network based on event-triggered conditions. The proposed adaptive backstepping algorithm not only ensures the security of the system under sensor attacks but also saves network resources and ensures the consistent tracking performance of multi-agent systems. The boundedness of all closed-loop signals is proved by Lyapunov stability analysis. Simulation examples show the effectiveness of the control scheme. [ABSTRACT FROM AUTHOR]

This paper studies the distributed optimal coordinated control problem for Euler–Lagrange multi-agent systems with connectivity preservation. The aim is to force agents to achieve the optimal solution minimizing the sum of the local objective functions while guaranteeing the connectivity of the communication graph. For practical purposes, the gradient vector of the local objective function is allowed to use only at the real-time generalized position instead of at the auxiliary system state. To make the control parameters independent of the global information and guarantee the fully distributed manner of controller, the adaptive control is introduced to update the coupling weights of the relative states among neighbors. Moreover, to reduce the resource for control updates, the event-driven communication is employed for the updates of both the relative states and the gradient of the connectivity-preserving potential function. Based on the Lyapunov analysis framework, it is proved that agents can converge to the optimal solution with connectivity preservation and Zeno behavior is excluded for the two event-triggering conditions. Finally, the effectiveness of the proposed method is verified by a numerical simulation example. [ABSTRACT FROM AUTHOR]

This paper proposes a disturbance observer-based neural adaptive stochastic control approach for the attitude control system of a class of Kinetic Kill Vehicles (KKVs) with unmeasured states and full state constraints. First, a one-one mapping is applied to transform the attitude control system with state constraints into a nonlinear novel system without any constraint. As a result, the control objective is changed into the boundness of the novel system states. Furthermore, the disturbances existing in the system are effectively estimated and eliminated by the nonlinear disturbance observer as well as the radial basis function neural networks (RBFNNs). Moreover, due to the dynamic signal, the dynamic uncertainty induced by the unmeasured states with an unknown dynamic is compensated appropriately. Utilizing the stochastic Lyapunov process, the boundness of all the signals in the system can be proven and the state constraints are satisfied. Finally, two groups of simulations are conducted, which demonstrate the remarkable performance of the proposed algorithm under different working conditions and highlight the advantages compared with existing studies. [ABSTRACT FROM AUTHOR]

This paper addresses the distributed control of delayed interconnected nonlinear systems with time-varying delays in both the local subsystems' dynamics and the physical interconnections among the subsystems. The Takagi–Sugeno fuzzy model with nonlinear consequent parts (N-TS), which is capable to provide less complex representations than standard T–S fuzzy models, is considered to efficiently deal with this class of complex systems. Then, based on Lyapunov–Krasovskii stability arguments, a synthesis condition is proposed to design a distributed control law such that the origin of the closed-loop interconnected system is locally asymptotically stable together with a guaranteed set of admissible initial conditions for which the validity of the N-TS fuzzy model is ensured. Moreover, a quasi-convex optimization procedure is formulated to enlarge the set of admissible initial conditions. The effectiveness of the proposed synthesis condition is validated in two numerical examples, including an interconnected power network with seven generators. [ABSTRACT FROM AUTHOR]

This paper investigates the robust output regulation problem for stochastic systems with additive noises. As is known, for the output regulation control problem, a general method is to regard that the system is disturbed by an autonomous exosystem (which is consisted by external disturbances and reference signals), and for the system disturbed by the white noise, the stochastic differential equations (SDEs) should be utilized in modeling, accordingly, a controller with a feedforward regulator is constructed for the stochastic system with an exosystem, which can not only cancel the external disturbance, but also transform the trajectory tracking problem into the stabilization problem; In consideration of the state variables in stochastic systems cannot be measured completely, we embed an observer to the controller, such that the random interference can be suppressed, and the trajectory tracking can be achieved. Based on the stochastic control theory, the criteria of the exponential practical stability in the mean square is presented for the closed-loop system, finally, through tuning the controller parameters, the mean square of the tracking error can converge to an arbitrarily small neighborhood of the origin. [ABSTRACT FROM AUTHOR]

In this paper, the fully distributed consensus for a class of multiple Euler-Lagrange systems is investigated, where the protocol is designed under the event-triggered control framework and the dynamics of Euler-Lagrange systems are heterogeneous. Since only local information interactions at triggered instants can be used and the Euler-Lagrange systems are of relatively complex dynamics, it is challenging to achieve asymptotic consensus without using any global information (such as the Laplacian matrix information). By skillfully integrating the adaptive control, distributed control and event-triggered control techniques, a novel protocol is proposed for the investigated multiple Euler-Lagrange systems. It is proven that the asymptotic consensus can be achieved by the developed protocol. By a numerical example, the effectiveness of the developed protocol is illustrated. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of robust adaptive attitude tracking control for spacecraft with mismatched and matched uncertainties. The idea of disturbance estimation and compensation is introduced into the control design. First, finite-time disturbance observers are developed for different channels of spacecraft based on barrier functions for achieving finite-time asymptotic estimates of unknown bounded uncertainties in the system. Second, a class of prescribed performance functions is considered in the design of the barrier function. The spacecraft attitude adaptive tracking control strategy with finite-time convergence capability and prescribed performance is proposed based on the designed finite-time disturbance observers and barrier function. Finally, the theoretical findings are verified by numerical simulations and compared with the simulation results of existing methods. [ABSTRACT FROM AUTHOR]

The problems of guaranteed cost control and H ∞ control are studied in this paper for nonlinear uncertain Itô's stochastic systems with time-varying delays in finite-time. It is assumed that the nonlinear function in the systems satisfies the quasi-one-sided Lipschitz nonlinear constraint condition, which is weaker and less conservative than the one-sided Lipschitz condition. Some novel sufficient conditions for the existence of guaranteed cost control strategies are derived by using the Gronwall's inequality, Dynkin's formula, and Lyapunov–Krasovskii functional. The state and output feedback's controllers based on event-triggered control guarantee that the closed-loop system is finite-time stochastically bounded and the given quadratic cost function has an upper bound. Finally, the effectiveness of the suggested method is illustrated by numerical simulation. [ABSTRACT FROM AUTHOR]

This paper investigates the finite-time cooperative circumnavigation control of multiple second-order agents, in which the agents should surround a moving target with desired formation and circular velocity based on local information. Firstly, the controller design is transformed into design control parameters such that the error system, including distance error, speed error and angle error, is finite-time consensus. The error system is viewed as a cascaded system containing two second-order subsystems, and then a distributed finite-time controller composed of two parts is delivered. The finite-time stability of the entire system is given by employing cascaded control theory. One significant advantage of the proposed controller is that it allows the agents to converge to desired trajectory in a finite time instead of asymptotically. Another merit is that the desired formation is an extensive case and unlimited, including different tracking radii and angular spacing. Furthermore, the proposed controller can be implemented by each agent in its local frame, utilizing only local information. These properties significantly extend the application scope of cooperative circumnavigation. Finally, simulations are carried out to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper studies the adaptive tracking control problem for a class of uncertain high-order fully actuated (HOFA) systems with actuator faults and full-state constraints. Firstly, we design a novel nonlinear transformation function (NTF) only related to state and constraint boundaries and capable of handling asymmetric time-varying constraints. With the designed function, we obtain an equivalent totally unconstrained HOFA model which is generally simpler to design controllers than first-order state-space model. Then, the adaptive fault-tolerant controller is constructed with the help of the HOFA approach. By applying the Lyapunov stability theory, it is rigorously proved that the output tracking error converges to zero asymptotically, other signals of the resulting closed-loop systems are bounded, and full-state constraints are not violated for all time. Finally, the simulation results verify the efficiency of the proposed control design method. [ABSTRACT FROM AUTHOR]

The helicopter is a highly unstable nonlinear non-minimum phase multiple-input multiple-output (MIMO) system with strong coupling between degrees of freedom. Therefore, its modeling will cause many problems such as control complexity. In addition, hover and forward flight modes are one of the most widely used helicopter flight modes used in many operations. To achieve and stay in these situations, you need very precise settings and timely manual control of the bird. This paper seeks to provide an L 1 adaptive-hybrid control method to solve helicopter control problems in the Koppler system. In this regard, three channels in the coupler system are considered for the design of this controller. The proposed controller also proves the stability of the system during switching between channels. The helicopter that is modeled in this research and its data is used to implement and simulate the Coupler system control is the SA330 Puma helicopter, which is a medium-sized twin-engine support helicopter, in the category of 6 tons, built by Eurocopter France (ECF), and is in service several domestic operators and the armed forces, including the British Air Force. This device is a completely unstable dynamic system. In addition to the inherent instability of this bird, by adding an external load hanging to this system, the challenge of controlling this system is doubled hence, it is necessary to maintain its stability by using a suitable control system. In the meantime, the modeling of the hanging load should be done in three dimensions and two planes, and like most of the work done, only the movement of the plane should not be considered. For this purpose, an L 1 adaptive-hybrid control method is proposed and fully developed in the paper, then it is properly designed for the Coupler system, which contains three channels: Pitch, Roll, and Collective, taking into account the load hanging on the helicopter. Finally, by simulating the results, it can be seen that the main goals, even in the presence of various disturbances, have the desired, efficient, and very good performance. [ABSTRACT FROM AUTHOR]

• Generalize the fixed-time control of nonholonomic systems with uncertainties driven by inputs and states from the stability problem to the consensus problem. • The control law proposed in this paper weakens the assumptions of similar references on the uncertainty functions. • The fixed-time adaptive distributed observer designed in this paper significantly speeds up the observation speed of the traditional adaptive distributed observer. And compared with the reference that proposes similar fixed-time adaptive distributed observer, the observer proposed in this paper ensures that global information does not appear in the control law. This paper discusses the fixed-time leader-following consensus problem for multiple uncertain nonholonomic systems, which are widely used in engineering models. According to our literature review, either the system is assumed to be known, or the uncertainty only contains state information, which does not meet the actual requirements. For this reason, this paper investigates more general nonholonomic systems with uncertainties driven by inputs and states. First, a fixed-time adaptive distributed observer is proposed to estimate the leader's state and structural parameters, which ensures that the estimation errors converge to zero within a fixed time. Second, two regulator equations based on the idea of cooperative output regulation are constructed, and a novel observer-based distributed switching control law is proposed. This control law overcomes the nonholonomic constraints and appropriately relaxes the assumptions of uncertain functions in the existing references. Finally, the simulation results verify the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Within the theoretical framework supporting the Model Free Adaptive Control approach, this paper investigates on the presence of possible loss of effectiveness affecting the actuator of a Single-Input Single-Output plant. In particular, stemming from a tracking controller proposed in a seminal paper and the related proofs, an identification algorithm is proposed for the estimation of the loss of effectiveness, with proved bounded estimation error, along with a condition for the detection of actuator faults. A fault-resilient tracking controller is finally proposed, based on the estimated loss of effectiveness, providing asymptotically vanishing tracking error. A comparative analysis by simulation on a benchmark system taken from the pertinent literature is also presented to validate the proposed development. • Resilience with respect to possible loss of actuator's effectiveness is addressed. • A data driven control framework using online plant I/O data only is considered. • A fault estimation algorithm with proved bounded error is proposed. • Inherent adaptation features are exploited and strengthened. • Tests show improved control resilience, authority and tracking accuracy. [ABSTRACT FROM AUTHOR]