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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 discusses the fuzzy adaptive consensus secure control problem for a class of stochastic nonlinear multi-agent systems (MASs) with unknown false data injection attacks. Fuzzy logic systems (FLSs) are used to model unknown nonlinear functions. A distributed filter is proposed to obtain the information of the leader since leaders information is only available to partial agents. To deal with the unknown virtual time-varying gains caused by false data injection attacks, an adaptive compensation method is developed via re-codesigning the virtual controllers. Based on backstepping control methodology and bounded estimation algorithms, an adaptive fuzzy consensus secure controller is proposed. It is proved that all the signals in the closed-loop systems are bounded in probability, and all the output of followers can track the leader under false data injection attacks. Finally, simulation results and comparative results show the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

The predefined-time fuzzy adaptive output feedback control problem is considered for non-strict feedback stochastic nonlinear systems with state constraints. Since the controlled plant contains unknown nonlinear dynamics and unmeasured states, the unknown nonlinear dynamics are handled by using fuzzy approximation technique, and a fuzzy state observer is established to estimate unmeasured states. Then, under the frameworks of a predefined-time stability theory and backstepping control design technique, a new fuzzy adaptive output feedback control method is proposed. It is proved that the controlled system is semi-global practically predefined-time stable in probability by constructing suitable barrier Lyapunov functions. Finally, the spring–mass–damper system is given to confirm the effectiveness of the presented control method. [ABSTRACT FROM AUTHOR]

The predefined-time fuzzy adaptive output feedback decentralised control problem is considered for fractional-order nonlinear large-scale (FONLS) systems. Firstly, by using fuzzy logic systems (FLSs) to model unknown nonlinear dynamics, a fuzzy decentralised state observer is designed to solve the immeasurable state problem. Secondly, the predefined-time stability criterion of fractional-order nonlinear systems is proposed, and then, based on the established predefined-time stability criterion of the fractional-order nonlinear systems, a predefined-time fuzzy adaptive output feedback decentralised control method is developed by combining the backstepping design technique with the cyclic-small-gain approach, and the semi-global practically predefined-time stable (SGPPTS) of the control system is proved. Finally, the numerical simulation is given and shows the effectiveness of the presented predefined-time control method. [ABSTRACT FROM AUTHOR]

The problem of finite-time mean-nonovershooting control is investigated for a class of stochastic nonlinear systems in this paper. For general stochastic strict-feedback nonlinear systems, a new controller is designed, which uses the framework of the backstepping technique, to assure that the mean of overshoot can be adjusted arbitrarily small (by changing controller gains) while all the states of the closed-loop system in finite-time to get bounded in probability. In contrast with corresponding deterministic systems, it has better performance that control gains did not rely on the initial conditions of system states and derivatives of the reference trajectory. Finally, a simulation example is showing the appealing performance of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, a new fuzzy adaptive resilient decentralized control method is presented for the nonlinear interconnected cyber-physical systems under false data injection (FDI) attacks. Fuzzy logic systems (FLS) are used to model unknown nonlinear dynamics and the FDI attacks are represented by the sum of the system states and unknown time-varying bounded functions. To deal with the problem of the system states are unavailable under FDI attacks, a new coordinate transformation is proposed for control design. By using the backstepping control design theory and Nussbaum function property, a fuzzy adaptive resilient decentralized control scheme is developed, where the Nussbaum functions are employed to deal with the unknown time-varying weight. Then, the stability of the controlled systems is proved by constructing a appropriate Lyapunov function. Finally, the effectiveness of the proposed fuzzy adaptive resilient decentralized control scheme is verified by the simulation and comparison results. • A new fuzzy adaptive resilient decentralized controller is designed for the nonlinear interconnected CPSs under FDI attacks. • The presented controller can ensure the controlled systems to be stable and compensate the impacts of FDI attacks. • the FLSs are utilized to approximate nonlinear functions and a new coordinate transformation under FDI attacks is given. [ABSTRACT FROM AUTHOR]

This paper studies the problem of adaptive backstepping control with full state triggering for a class of parametric uncertain nonlinear systems. The use of triggering mechanism discretizes measured states, which causes the issue of nondifferentiation for virtual control signals and hinders the design of adaptive estimation mechanism for unknown parameters in the traditional backstepping design. To conquer the difficulties, a group of adaptive chainlike filters are designed to generate necessarily smooth state estimates, with adaptive parameters online adjusted by the triggering state estimation errors. It is shown that the designed event-triggered mechanism allows the proposed adaptive backstepping control scheme to achieve asymptotic convergence even in the presence of state discretization. [ABSTRACT FROM AUTHOR]

This article focuses on the issue of addressing an adaptive neural network (NN) inverse optimal containment control for nonlinear multiagent systems (MASs), which are subject to immeasurable states and quantized input signals simultaneously. To tackle this problem, we utilize a NN to model unknown agents and design a NN observer to estimate the immeasurable states. Additionally, we decompose the hysteretic quantized input into two bounded nonlinear functions. By employing the adaptive backstepping approach and inverse optimal principle, we formulate an adaptive NN inverse optimal containment control method. The developed inverse optimal containment control scheme guarantees that the controlled system is input-to-state stabilizable (ISS). Finally, we validate the effectiveness of our proposed control scheme through simulation results. [ABSTRACT FROM AUTHOR]

In this work the fuzzy adaptive control issue is investigated for multiple‐input multiple‐output (MIMO) uncertain nonlinear systems in strict‐feedback form. The controlled systems under consideration of this work contain the immeasurable states, unknown control gain functions and input time delays. The immeasurable states are estimated by constructing a fuzzy state observer, and the uncertain nonlinear functions are approximated by utilizing the fuzzy logic systems (FLSs). In addition, the Pade approximation method is employed to handle the input time delays problem. An observer‐based fuzzy adaptive centralized control algorithm is presented by employing the adaptive backstepping control design technique and constructing the Logarithm Lyapunov functions. The designed fuzzy adaptive robust control algorithm can make sure that all signals in the closed‐loop system are semi‐globally uniformly ultimately boundedness (SGUUB). Finally, a numerical simulation example and a practical system example are considered to testify the availability of the designed centralized controllers. [ABSTRACT FROM AUTHOR]

This paper solves the fault-tolerant control (FTC) problem of uncertain nonlinear systems in nonstrict-feedback form. The controlled plants considered in this paper are more complicated than existing ones, which is composed of the unknown nonlinearities, unmeasured states, and sensor faults and unknown dead zone input nonlinearity. Fuzzy logic systems and a fault-estimation-based state observer are used for identifying the unknown nonlinearities and obtaining the unmeasured states, respectively. By designing the sensor faults compensation and the dead zone inverse compensation methods, an observer-based fault-tolerant compensation control scheme is developed under backstepping recursive design frame. And it is testified that the closed-loop signals are bounded, and the tracking performance is satisfied even when the controlled systems are not free of sensor faults and unknown dead zone input nonlinearity. An electromechanical system is used to test the effectiveness of the developed control strategy. [ABSTRACT FROM AUTHOR]

Summary: This article studies the adaptive fuzzy finite‐time quantized control problem of stochastic nonlinear nonstrict‐feedback systems with full state constraints. During the control design process, fuzzy logic systems are used to identify the unknown nonlinear functions, integral barrier Lyapunov functions are employed to solve the state constrained problem. In the frame of backstepping design, an adaptive fuzzy finite‐time quantized control scheme is developed. Based on the stochastic finite‐time Lyapunov stability theory, it can be guaranteed that the closed‐loop system is semiglobal finite‐time stable in probability, and the tracking errors converge to a small neighborhood of the origin in a finite time. Finally, two simulation examples are provided to testify the effectiveness of the developed control scheme. [ABSTRACT FROM AUTHOR]

In this paper, the event-triggered adaptive neural network (NN) asymptotic consensus control problem is studied for nonlinear multi-agent systems (MASs) with unknown measurement sensitivity. A new distributed filter is designed to estimate the leader and solve the nondifferential problem of virtual controllers caused by the noncontinuous consensus error. The unknown nonlinear dynamics and measurement sensitivity are handled by using the NN approximators and adaptive estimation method, respectively. To avoid unnecessary data transmissions, a dynamic event-triggered mechanism (DETM) composed of the measurement and control channels is designed. Then based on backstepping technique, an asymptotic consensus controller is obtained. It is proved that the closed-loop signals are all bounded, and the consensus error asymptotically converges to the origin. Finally, to show the effectiveness of the developed control method, we apply the developed control method to multiple unmanned surface vehicles (USVs). [ABSTRACT FROM AUTHOR]

This paper studies the problem of resilient distributed cooperative control for a class of uncertain nonlinear multi-agent systems (MASs) under Denial-of-Service (DoS) attacks. To avoid repeatedly differentiating the discontinuous communication signals caused by the DoS attacks in the backstepping procedure, a new circular switching filter is constructed. Compared with the existing results, the circular switching filter can sufficiently improve the tolerance capacity for the DoS attacks in the sense that the DoS attacks can be tolerated as long as the network under attacks is intermittently connected. To address the issue of discontinuity of the switching controller, a bumpless transfer controller is further proposed by smoothing the discontinuous communication signals based on an artificial time delay mechanism. Simulation results illustrate the effectiveness of the proposed control schemes. [ABSTRACT FROM AUTHOR]

In this paper, a reinforcement Q-learning fuzzy optimal tracking control method is investigated for nonlinear underactuated unmanned surface vehicles (USVs) with external disturbances. Firstly, the motion dynamics of USVs are described by the Takagi–Sugeno (T–S) fuzzy discrete-time systems. Secondly, by applying parallel distributed compensation (PDC) method and constructing cost function, the existence condition of optimal solutions is derived and reduced to the algebraic Riccati equations (AREs). To solve the solutions of the GAREs, a Q-learning value iteration (VI) algorithm is further developed, which can be implemented by eliminating the demand of initial allowable control policy and system information. The developed strategy has the advantage that the optimal control policy can ensure the USV to be stable and the desired reference signal can also be well tracked by the USV output. Finally, the proposed fuzzy optimal control algorithm is applied to control the USV, the simulation results verify the feasibility of the proposed optimal control algorithm. • A fuzzy optimal control strategy for underactuated USVs is designed in this study. The designed fuzzy optimal controller can make the output of underactuated USV track the desired reference trajectory. Though the authors in (Wang et al. (2018); Hao et al. (2021); Ma et al. (2020); Zhang et al. (2021); Song et al. (2023)) also investigated the fuzzy control design problems of the USVs, they did not study optimal control design problem and thus the optimal control performances of USVs cannot be achieved like this paper. • This paper proposes a fuzzy optimal tracking control algorithm via convergent Q-learning VI which can obtain the optimal control input policy of underactuated USVs. Although the existing results (Zhang et al. (2019b); Zhang et al. (2019a); Zhang et al. (2020); Wang et al. (2022b)) also investigated the fuzzy optimal tracking control design problems, they ignored the ocean disturbances existing in USVs systems, thus they have no robustness against the ocean disturbances. In addition, though the authors in (Wang et al. (2022c)) proposed fuzzy optimal controller, it did not investigate the fuzzy optimal tracking control design problem of USVs. [ABSTRACT FROM AUTHOR]

In this paper, a novel fuzzy adaptive output-feedback control design scheme is concerned for a class of non-strict feedback switched nonlinear systems. The considered nonlinear system contains arbitrary switching, unmodeled dynamics, unmeasured states, and unknown nonlinearities. In the design process, fuzzy logic systems (FLSs) are utilised to identify the unknown nonlinear functions. A state observer without depending on the switching signal is constructed to deal with the unmeasured states. By combining adaptive backstepping with dynamic surface control (DSC) technique, a fuzzy adaptive control scheme is presented to overcomes the 'explosion of complexity' problem. It is proved that all the signals of the closed-loop system can be bounded by the common Lyapunov function theory. Finally, a simulation example is provided to elaborate the feasibility and effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

This paper addresses the finite time adaptive fuzzy decentralized control problem for interconnected large scale nonlinear systems in nonstrict feedback forms with output-constraint. The fuzzy logic systems are used to approximate the unknown nonlinear functions and a state observer is constructed to estimate the immeasurable states. In order to deal with the output constraint problem, the barrier Lyapunov function is introduced. By combining backstepping recursion design with a command filter, a finite time fuzzy adaptive decentralized control method is presented. The stability analysis can be obtained based on the finite time Lyapunov stability theory, which demonstrates that the closed-loop system are semi-global practical finite-time stability, the system outputs can track the given reference signals and keep in the given constraint bounds in a finite time. Finally, two simulation examples are provided to elaborate the effectiveness of the presented control scheme. [ABSTRACT FROM AUTHOR]

This paper studies the non-singular practical fixed-time neural adaptive control issues for multi-input and multi-output (MIMO) nonlinear systems with non-strict feedback form. Neural networks (NN) are used to estimate the unknown nonlinearities and deal with the problem of an algebraic loop. Under the framework of the backstepping control design, a practical fixed-time adaptive NN control method is developed by using the adding power integration technology. According to the Lyapunov function theory, it is proved that the closed-loop system is practical fixed-time stable, and the system can track the desired reference signal within a fixed time. Finally, the proposed practical fixed-time control method is applied to a multi-motor control platform, which proves the effectiveness of the control method. [ABSTRACT FROM AUTHOR]

In this paper, a novel fuzzy adaptive tracking control scheme is presented for a class of nonstrict-feedback uncertain switched nonlinear systems by taking into account constraint. The considered switched nonlinear system contains unknown nonlinearities. In the design process, firstly, the problem of state constraints is coped with by utilizing the asymmetric time-varying barrier Lyapunov functions (TABLFs), and the problem of "explosion of complexity" is solved by dynamic surface control (DSC) technology. Secondly, fuzzy logic systems (FLSs) are utilized to approximate the unknown nonlinear dynamics. Under the framework of adaptive control design, the switched adaptive state feedback controller is designed, which can guarantee all states cannot violate their constrained sets. Then, based on the Lyapunov function and average dwell time (ADT) approach, the theoretical analysis is provided for the closed-loop systems within constraint performance bounded. Finally, a numerical example is proposed to further elaborate the effectiveness of the presented control scheme. [ABSTRACT FROM AUTHOR]

In this article, the problem of output feedback robust control for heterogeneous vehicle platoon system (HVPS) is investigated. A novel double‐channel event‐triggered mechanism is designed to reduce the waste of communication resources and unnecessary data transmissions. A neural network state observer is designed by intermittent output signal to estimate unmeasurable states. The prescribed performance function is used to establish a strong string stability condition. Combining backstepping recursive technique and prescribed performance function with its inverse transformation, observer‐based robust adaptive output feedback event‐triggered platoon controller is designed, which can ensure the individual stability, string stability, and strong string stability of HVPS. It is proved that all signals are bounded by Lyapunov stability analysis. Finally, the validity of the proposed results is proved by simulation. [ABSTRACT FROM AUTHOR]

Summary: This article investigates the adaptive neural network (NN) output‐feedback event‐triggered consensus secure control problem for a class of nonlinear multi‐agent systems (MASs) under mixed sensor attacks and actuator faults. Since the considered nonlinear MASs contain unknown nonlinear dynamics, the NNs are first adopted to model unknown agents. Then, a nove NN learning secure state observer is proposed to estimate the sensor attacks and unmeasured states. To reduce unnecessary updating times of the actuator, an event‐triggered mechanism is constructed. By using the backstepping control design technique and the design NN state observer, a NN adaptive output‐feedback event‐triggered consensus secure control scheme is formulated. It is proved that the developed consensus secure control scheme can guarantee the controlled nonlinear MASs are stable and consensus tracking errors converge even under mixed sensor attacks and actuator faults. Simulation and comparative results illustrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This article studies the fixed-time fuzzy formation tracking control problem for multiple unmanned surface vehicle (USV) systems with intermittent actuator faults. Since each USV is subject to unknown motion dynamics and the external disturbances. The fuzzy state observers are developed to estimate the positions and velocity in multiple USV systems, and disturbance observers are developed to estimate the external disturbances, and fuzzy logic systems are utilized to identify the unknown motion dynamics. Then, to compensate for the influence of intermittent actuator faults, the novel fixed-time adaptive output-feedback formation fault-tolerant controllers are constructed by constructing the integral type Lyapunov function and co-designing the last virtual controller. The presented formation control method can guarantee all the signals in multiple USV systems are bounded in fixed-time. Finally, simulation results and comprehensive comparisons are provided to verify the effectiveness of the presented formation control method. • This paper proposes an observer-based fuzzy formation FTC approach for multiple nonlinear USV systems. The presented formation FTC can achieve to compensate for the influence of IAFs. Although the recent works (Wang, 2021 ; Zhang et al., 2022) also address the FTC problem for nonlinear USVs, their FTC method only deal with a finite of actuator fault and can not be applied to control nonlinear multiple USV systems with immeasurable states in this study. [ABSTRACT FROM AUTHOR]

Abstract This paper studies the problem of fuzzy adaptive finite-time output feedback control for a class of multi-input and multi-output (MIMO) nonlinear systems with errors constraint and unknown dead zone. In control design, the fuzzy logic systems (FLSs) are adopted to approximate the unknown nonlinear functions and a state observer is constructed to estimate the unmeasurable states. The simplified barrier Lyapunov function (SBLF) and performance function are considered to solve the problem of errors-constraint. Combining backstepping design with prescribed performance technique, an observer-based finite-time adaptive fuzzy control scheme is presented. The presented control scheme not only guarantees that all signals of the closed-loop system are bounded, but also ensures that tracking errors converge to a small neighborhood of the zero within the prescribed performance bounded. Finally, a numerical example is provided to elaborate the effectiveness of the presented control scheme. [ABSTRACT FROM AUTHOR]

Summary: This article considers the issue of fuzzy adaptive dynamic programming control of nonlinear switched systems with arbitrary switchings and unknown uncertain functions and actuator hysteresis nonlinearities. The whole control approach is made of switching feedforward controller and optimal switching feedback controller. To get over the hardness of arbitrary switching structure and the issue of "explosion of complexity", the common Lyapunov function theory and dynamic surface control method are utilized in the recursive design technique. By using fuzzy logic systems to model unknown inner dynamics and unknown cost functions, a novel fuzzy adaptive optimal switching control strategy is developed. Meanwhile, uniformly ultimately boundedness of all weights in the controlled systems are proved by the proposed control method, and the tracking performance is guaranteed in an optimal manner. Subsequently, a numerical simulation study is used to test the effectiveness of the presented control strategy. [ABSTRACT FROM AUTHOR]

Summary: This paper focuses on consensus quantized control design problem for uncertain nonlinear multiagent systems with unmeasured states. Every follower can be denoted through a system with unmeasurable states, hysteretic quantized input, and unknown nonlinearities. Fuzzy state observer and Fuzzy logic systems are employed to estimate unmeasured states and approximate unknown nonlinear functions, respectively. The hysteretic quantized input can be split into two bounded nonlinear functions to avoid chattering problem. By combining adaptive backstepping and first‐order filter signals, an observer‐based fuzzy adaptive quantized control scheme is designed for each follower. All signals exist in closed‐loop systems are semiglobally uniformly ultimately bounded, and all followers can accomplish a desired consensus results. Finally, a numerical example is employed to elaborate the effectiveness of proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper concerned with the problem of observer-based adaptive fuzzy quantized tracking dynamic surface control (DSC) is investigated for the uncertain multi-input and multi-output (MIMO) nonstrict-feedback nonlinear systems, which contain unknown nonlinear functions, input quantization, and unmeasured states. By using fuzzy logic systems to identify the uncertain MIMO nonstrict-feedback nonlinear systems, a fuzzy state observer is introduced to estimate the immeasurable states. By transforming the hysteretic quantized input into a new nonlinear decomposition, and utilizing the DSC backstepping design method, a novel and less conservative fuzzy adaptive quantized tracking control approach is developed. It is shown that the proposed control scheme can guarantee the stability of the closed-loop system, and also that the system outputs can track the given desired trajectories. The simulation results are provided to verify the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Summary: This paper investigates the adaptive fuzzy prescribed performance output‐feedback decentralized control problem for a class of stochastic interconnected nonlinear large‐scale systems. The fuzzy logic systems are used to approximate the unknown nonlinear functions, and a state observer is designed to estimate the unmeasured states. By combining the backstepping recursive design principle with prescribed performance theory, an adaptive fuzzy decentralized control method is presented. In order to overcome the problem of "explosion of complexity" in the adaptive backstepping control design, the first‐order filter signals are introduced into adaptive fuzzy decentralized control design algorithm and form a new simplized. The stability is proven based on the Lyapunov stability theory, which demonstrated that all the signals of the closed‐loop system are semiglobally uniformly ultimately bounded in probability and the tracking errors remain a small neighborhood of the origin within the prescribed performance bounds. Finally, a simulation example is provided to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The decentralized adaptive fuzzy control problem for switched nonstrict-feedback nonlinear large-scale systems is investigated in this paper. Fuzzy logic systems are adopted to identify the unknown nonlinear system functions and unknown interconnected nonlinearities. A linear state observer is designed to solve the problem of unmeasured states, and a new observer-based output-feedback decentralized control scheme is developed based on adaptive fuzzy control principle and bacsktepping design technique. The proposed control strategy not only solves the control design problem of more general form nonlinear switched systems in nonstrict-feedback form, but also does not require the switched signals to meet the restriction of average dwell time. The stability of fuzzy control systems under arbitrary switchings is proven based on the common Lyapunov function method. Simulation results of numerical examples are presented to further demonstrate the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Fuzzy adaptive optimal bounded control problems are first investigated for a class of nonlinear continuous-time interconnected systems whose system internal dynamics and unmatched interconnections are completely unknown, when there exist unavailable states in the subsystems of the interconnected systems. The system states and the interconnection terms of the interconnected system are approximated by using a fuzzy state observer. The decentralized optimal controllers and observer-critic structure are designed according to adaptive dynamic programming and enforcement learning technology. The presented control methods can ensure that the system states and parameter estimation errors of the interconnected systems are ultimately uniformly bounded. A simulation example validates the effectiveness of the presented scheme. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of adaptive fuzzy tracking control for a single-link robotic manipulator coupled to a brushed direct current motor with actuator saturation. Fuzzy logic systems are used to approximate the unknown nonlinear systems. The filtered signals are introduced to eliminate the interference of high-frequency signals. The virtual/actual control inputs are derived from the solutions of a series of dynamical equations. Under the framework of the backstepping control design, an observer-based output feedback control design scheme is proposed. It is proved that the control approach can guarantee that all the signals in the closed-loop system are bounded. [ABSTRACT FROM AUTHOR]

This study develops a scheme of an adaptive fuzzy dynamic surface control (DSC) for a non‐linear non‐strict feedback system with immeasurable states. By utilising the universal approximation property of a fuzzy logic system, a state observer is designed to estimate the unmeasured states. Based on the back‐stepping DSC design technique, an adaptive fuzzy output feedback control design method is developed. The proposed method can overcome the 'explosion of complexity' problem, and also can eliminate the limitation of the measurable states. It is proved that the control method can guarantee that all the signals in the closed‐loop system are bounded and the system output can track the given reference signal well. The simulation results are provided to demonstrate the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

An output-feedback control problem is investigated for a class of T-S fuzzy switched stochastic systems with input delay in the paper. The considered fuzzy switched stochastic systems consist of several switched stochastic models, and each is described by T-S fuzzy system. Based on the parallel distributed compensation method, a fuzzy output-feedback controller is developed. Furthermore, the sufficient conditions are derived to guarantee the fuzzy switched stochastic system with input delay pth moment is exponentially stable. Finally, two examples are given to illustrate the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

This article investigates the adaptive control problem for nonlinear cyber‐physical systems with network communication encountered false data injection (FDI) attacks. To address such attacks, the attack estimate method is designed whose objective is to minimize the vulnerability of FDI attacks. This article aim to find, using the historical FDI attack, a solution with guaranteed out‐of‐sample forecasting, so as for the attacker to plan its attacks such that the worst possible action on the system measurement. The approach is to formulate a robust optimization problem using the box‐like sets, and then transform it into a linear programming model for solving problems. Consequently, under the framework of backstepping, a robust adaptive state‐feedback control method is proposed. By using Lyapunov stability theory, the proposed control scheme can guarantee that all the closed‐loop signals are globally bounded and the stabilization error converges to the origin. Finally, simulation results illustrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

In recent years, since the unique advantages in automotive structures, the vehicle active suspension systems have received widespread attentions. A good active suspension system can reduce the vibration and improve the overall performance of the vehicle. Therefore, the design of the controller for the active suspension system to perform autonomous adjustment plays a vital role in vehicle comfort and safety. For the active suspension of the seven-DOF sport utility vehicle (SUV) model, this paper takes the vehicle body acceleration, tire dynamic load and suspension dynamic travel as the indicators to evaluate the performance, and the proportional-integral-derivative (PID) controller is designed to improve the performance of the vehicle active suspension system. Based on the software of MATLAB/Simulink and Carsim, a closed-loop co-simulation model diagram is established, which includes a PID controller module. Meanwhile, the random road input model and the whole vehicle model are constructed in Carsim. Finally, at the speeds of 70, 90, and 120 km/h, the active suspension system under the designed PID controller is simulated and compared with the passive suspension system. The simulation results show that the active suspension system based on PID controller can effectively improve the overall performance of the vehicle, and then the comfort and safety of the vehicle can be further enhanced. [ABSTRACT FROM AUTHOR]

This paper investigates the neural network (NN) adaptive consensus output-feedback control problem for a class of nonlinear multi-agent systems (MASs) encountered sensor attacks. To overcome the impact of unknown sensor attacks, a NN estimation algorithm is adopted to estimate the unknown sensor attacks. Subsequently, a novel NN observer is established to estimate the states of encountered sensor attacks. Consequently, under the framework of backstepping control design, an adaptive NN consensus control method is proposed. By using the Lyapunov stability theory, the proposed consensus control method can not only ensure that all the signals of controlled MASs remain bounded, but also make all followers maintain consensus with the trajectory of the leader. Simulation results and comparative results illustrate the effectiveness of the proposed consensus control scheme. [ABSTRACT FROM AUTHOR]

In this paper, an output feedback control method with prescribed performance is proposed for single-input and single-output (SISO) switched non-strict-feedback nonlinear systems. It is assumed that nonlinear functions of the concerned systems are unknown, switching signals are unknown and arbitrary, and the states are unmeasured. A linear state observer is designed to estimate the unmeasured states, and an observer-based output feedback control scheme is developed. The key advantages of the proposed control strategy are that virtual control gains of the concerned non-strict-feedback nonlinear systems are not required to be known, and only one tuning parameter is needed. Based on Lyapunov stability theory, it is shown that all the signals in the resulting closed-loop system are semi-globally uniformly ultimately bounded, and the tracking error converges to a small residual set with the prescribed performance bound. The effectiveness of the proposed control approach is verified by a numerical example. [ABSTRACT FROM AUTHOR]

This paper considers the adaptive fuzzy output feedback control design problem for a class of switched nonlinear systems. The considered switched nonlinear systems contain unknown nonlinearities, the switching signals with average dwell time, and without requiring that state variables are available for measurement. Fuzzy logic systems are used to approximate the unknown nonlinearities, and a fuzzy switched state observer is designed to estimate the immeasurable states. In the framework of observer-based output feedback control design technique, and by introducing a command filter into the backstepping design algorithm, a command filter-based adaptive fuzzy backstepping output feedback control scheme is developed. The stability of the closed-loop system and the convergence of the tracking error are proved via average dwell time and multiple Lyapunov function. A numerical example is provided to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive controller design is studied for single-input–single-output (SISO) nonlinear systems with parameter uncertainties and the systems are enforced to subject to the full state constraints. A remarkable feature of the constrained systems is that the so-called control direction is unknown, or in other words, the signs of control gains are unknown. In the existing results, we discover that all the state constraint control results are required to determine a priori knowledge of control direction. It will be inevitable to bring about a different design procedure and a difficult task when no a priori knowledge of control direction is known. To stabilize this class of systems, two parameter adaptive controllers with Nussbaum gain technique are constructively framed to overcome the unknown control direction problem, and the novel symmetric and asymmetric Barrier Lyapunov Functions (BLFs) are adopted to guarantee that the states are not to violate their constraints. Then, the proposed BLF strategy can be to conquer the conservatism of the traditional BLF-based controls for the full state constraints. Finally, two theorems are provided to show that all the signals in the closed-loop system are bounded, the outputs are driven to follow the reference signals and all the states are ensured to remain in the predefined compact sets. The effectiveness of the proposed scheme is performed via a simulation example. [ABSTRACT FROM AUTHOR]

This paper investigates the output-feedback control design problem for a class of switched Takagi–Sugeno fuzzy large-scale systems with non-measurable premise variables. The considered fuzzy large-scale systems consist of several interconnected subsystems with different switching modes and there exists the asynchronous switching between the system switching modes and the controller switching modes. A decentralised state observer is proposed to estimate the unmeasured states, and a new output-feedback decentralised control scheme is developed by using the state observers and the switching functions. Based on the theory of Lyapunov stability and average dwell-time methods, the sufficient conditions of ensuring the switched control system stability are proposed and proved, which are formulated in the form of linear matrix inequalities. An applicable example is provided to show the effectiveness of the obtained theoretical results. [ABSTRACT FROM AUTHOR]

In this study, the fault‐tolerant control problem is investigated for a class of non‐affine output‐constrained multi‐input multiple‐output non‐linear systems with actuator faults. The controlled systems contain unknown non‐linear functions, unknown external disturbance and immeasurable states. The fuzzy logic systems and a robust compensation function are employed to deal with the unknown non‐linear functions and non‐affine non‐linear actuator fault problems. Then, the state observer and the non‐linear disturbance observer are developed for estimating the immeasurable states and unknown compounded disturbance, respectively. By using the barrier Lyapunov function method and in the unified framework of adaptive backstepping output feedback control design, a novel adaptive fuzzy fault‐tolerant controller design scheme is developed. It is shown that all the variables of the closed‐loop system are semi‐globally uniformly bounded. Moreover, the system outputs cannot violate their predefined bounds in the presence of the non‐affine non‐linear faults. A simulation is given to validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of fault-tolerant control (FTC) for a class of switched nonlinear systems. These systems are under arbitrary switchings and are subject to both lock-in-place and loss-of-effectiveness actuator faults. In the control design, fuzzy logic systems are used to identify the unknown switched nonlinear systems. Under the framework of the backstepping control design, FTC, fuzzy adaptive control and common Lyapunov function stability theory, an adaptive fuzzy control approach is developed. It is proved that the proposed control approach can guarantee that all the signals in the closed-loop switched system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error remains an adjustable neighbourhood of the origin. Two simulation examples are provided to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, a fuzzy adaptive switched control approach is proposed for a class of uncertain nonholonomic chained systems with input nonsmooth constraint. In the control design, an auxiliary dynamic system is designed to address the input nonsmooth constraint, and an adaptive switched control strategy is constructed to overcome the uncontrollability problem associated withx0(t0) = 0. By using fuzzy logic systems to tackle unknown nonlinear functions, a fuzzy adaptive control approach is explored based on the adaptive backstepping technique. By constructing the combination approximation technique and using Young's inequality scaling technique, the number of the online learning parameters is reduced tonand the ‘explosion of complexity’ problem is avoid. It is proved that the proposed method can guarantee that all variables of the closed-loop system converge to a small neighbourhood of zero. Two simulation examples are provided to illustrate the effectiveness of the proposed control approach. [ABSTRACT FROM AUTHOR]

This paper investigates the adaptive fuzzy output-feedback control problem for single-input and single-output switched uncertain nonlinear systems. The addressed systems in this paper have the characteristics of arbitrary switchings, unknown nonlinear dynamics and immeasurable states. A common state observer is designed independent of switching signals. Fuzzy logic systems are utilized to approximate unknown lumped nonlinear dynamics. Based on the framework of backstepping design technique, an adaptive fuzzy output-feedback control scheme is developed. By using the common Lyapunov function theory, the stability of the closed-loop system is proved. The proposed control scheme does not need the assumptions that the states of the controlled system are available for measurement and that the switching signals satisfy the average dwell time. Moreover, it can guarantee that all the closed-loop signals are bounded, and the system output eventually converges to a small neighborhood of the origin. Finally, simulation studies are provided to further check the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper studies the fuzzy finite-time reliable dynamic positioning (DP) control for nonlinear unmanned marine vehicles. Firstly, the motion dynamics of nonlinear unmanned marine vehicles is modeled by the Takagi-Sugeno (T-S) fuzzy system. Secondly, a fuzzy state observer is designed to estimate the immeasurable position and velocity of unmanned marine vehicles. In order to decrease the frequency of data transmission of actuators and sensors, a dynamic event-triggered mechanism is presented based on unmanned marine vehicles information and the estimation error. Then, a fuzzy event-triggered reliable output feedback control method is presented by finite-time theory to tackle the actuator faults problem occurring in unmanned marine vehicles. The sufficient finite-time stable conditions in the form of LMIs are established via constructing finite-time Lyapunov-Krasovskii functional. Ultimately, the computer simulation results and comparisons with the existing control methods verify the advantages of the presented control method. [ABSTRACT FROM AUTHOR]