Asymptotic tracking control for constrained nonstrict‐feedback MIMO nonlinear systems via parameter compensations

This article addresses an event-triggered adaptive fuzzy tracking control problem for nonlinear systems with unknown control directions. To achieve the practical tracking, fuzzy logic systems are employed to approximate unknown nonlinear function. Nussbaum functions are adopted to address the problem of the unknown control directions. The newly designed controller not only guarantees the tracking error that converges to an arbitrary small neighborhood near the zero but also reduces the communication burden from the controller to the actuator. Moreover, the feasibility of the proposed event-triggered mechanism is verified by excluding Zeno behavior. Finally, the simulation result proves the effectiveness of the designed control scheme.

Adaptive fuzzy control for full states constrained systems with nonstrict-feedback form and unknown nonlinear dead zone

Adaptive fuzzy tracking control for a class of uncertain nonaffine nonlinear systems with dead-zone inputs

Adaptive tracking control for a class of switched uncertain nonlinear systems under a new state-dependent switching law

This paper investigates an adaptive fuzzy tracking control problem for nonlinear stochastic nonstrict-feedback systems with unknown control directions and actuator faults. Based on the monotonically increasing property of system bounding functions, a variable separation scheme is invoked to overcome the research difficulty from nonlower triangular structure. Combining adaptive backstepping method with fuzzy logic system's approximation capability, an adaptive fuzzy fault tolerant controller is designed. The presented control algorithm makes not only the tracking error as small as possible but also the closed-loop trajectory bounded in the sense of the four-moment. Finally, simulation results are depicted to address the effectiveness of the studied control method.

Adaptive fuzzy tracking control for a class of pure-feedback stochastic nonlinear systems with non-lower triangular structure

In this article, the problem of event‐triggered adaptive asymptotic tracking control (ATC) for stochastic nonlinear systems with unknown control directions (UCDs) and full state constraints is concerned. It must be said that the controller design and system analysis is more complex and difficult since the existence of stochastic disturbances, UCDs and full state constraints simultaneously. By introducing the lower bound of the UCDs into the barrier Lyapunov functions, an event‐triggered adaptive MTN ATC scheme is proposed based on the boundary estimation method and a new event‐triggered control (ETC) strategy, which can achieve satisfactory asymptotic tracking performance and control performance of the system, while reduce the occupation of network resources. The simulation results not only verify the effectiveness of the proposed control scheme, but also present different tracking performances between three ETC strategies for comparison, further confirming the superiority of the proposed ETC strategy in achieving asymptotic tracking performance.

In this paper, the problem of adaptive fuzzy output-feedback control is investigated for a class of uncertain switched nonlinear systems in strict-feedback form. The considered switched systems contain unknown nonlinearities, dead-zone, and immeasurable states. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, a switched fuzzy state observer is designed and thus the immeasurable states are obtained by it. By applying the adaptive backstepping design principle and the average dwell time method, an adaptive fuzzy output-feedback tracking control approach is developed. It is proved that the proposed control approach can guarantee that all the variables in the closed-loop system are bounded under a class of switching signals with average dwell time, and also that the system output can track a given reference signal as closely as possible. The simulation results are given to check the effectiveness of the proposed approach.

This paper investigates the adaptive fuzzy observer-based tracking control problem for switched nonlinear systems with full state constraints and actuator failures under the switching law with average dwell time. Since the states in the system are not measurable, a nonlinear fuzzy observer is designed to estimate the immeasurable states. At the same time, the Tangent Barrier-Lyapunov functions are selected to solve the full-state constraint problem. In addition, because of the existence of unknown parameter in actuator failures, the adaptive approach is used. Further, owing to actuator failures in controller, which may lead to some subsystems to be unstable, the switching law under average dwell time is adopted. Thus, even if there exist the actuator failures and partially unstable subsystems, the proposed method can guarantee that all signals in the closed-loop system are bounded, the system output can track the desired signal, and all system states satisfy their corresponding constrained conditions. Moreover, the observer states can well estimate the immeasurable states. Finally, the effectiveness of the presented control scheme is verified by a simulation example.

This paper investigates adaptive fuzzy output feedback fault-tolerant optimal control problem for a class of single-input and single-output nonlinear systems in strict feedback form. The considered nonlinear systems contain unknown nonaffine nonlinear faults and unmeasured states. Fuzzy logic systems are used to approximate cost function and unknown nonlinear functions, respectively. It is assumed that the states of the systems to be controlled are unmeasurable, thus an adaptive state observer is developed. To solve the nonaffine nonlinear fault control design problem, filtered signals are introduced into the adaptive backstepping control design procedures, and in the framework of adaptive critic technique and fault-tolerant control technique, a novel adaptive fuzzy fault-tolerant optimal control scheme is developed. The stability of the closed-loop system is proved by using Lyapunov stability theory. The simulation results verify the effectiveness of the proposed control strategy.

Adaptive fuzzy finite-time tracking control of nonlinear systems with unmodeled dynamics

In this paper, an event-based adaptive fuzzy tracking control strategy for a class of uncertain nonlinear systems with limited control input and its rate of change is proposed. The unknown nonlinear function of the considered system is approximated by a fuzzy logic system (FLS). Furthermore, an event-triggered control method is developed to save the computing resources, in which the controller parameters are adaptively adjusted by a fuzzy controller. Moreover, it is proved by designing the Lyapunov function that the event-based controller can ensure that all signals of the controlled system are bounded. In addition, the tracking error can converge to a preset domain. Meanwhile, two practical simulations are given to demonstrate the effectiveness of the proposed method.

This paper focuses on the problem of approximation-based adaptive fuzzy tracking control for a class of stochastic nonlinear time-delay systems with a nonstrict-feedback structure. A variable separation approach is introduced to overcome the design difficulty from the nonstrict-feedback structure. Mamdani-type fuzzy logic systems are utilized to model the unknown nonlinear functions in the process of controller design, and an adaptive fuzzy tracking controller is systematically designed by using a backstepping technique. It is shown that the proposed controller guarantees that all signals in the closed-loop system are fourth-moment semiglobally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood of the origin in the sense of mean quartic value. Simulation results are provided to demonstrate the effectiveness of our results. Further developments will consider how to generalize the proposed strategy to nonstrict-feedback nonlinear systems with input nonlinearities.

The aim of this paper is to tackle the problem of adaptive fuzzy voltage-based tracking control for uncertain electrically driven robotic manipulators subject to input delay and partial state constraints in a unified framework. With the aid of barrier Lyapunov function-based backstepping method and adaptive fuzzy approximators, the proposed method is constructed for uncertain robotic systems in the framework of voltage control strategy. This is intended to convert robot control problem to motor control problem. Based on input integral technique, a new variable is introduced for the system such that the input-delayed robotic system is turned to the non-delayed robotic system. Furthermore, the number of adaptive learning parameters is free from the number of subsystems. In other words, only one adaptive parameter is adjusted online for each joint to reduce computational burden; hence, a new adaptive fuzzy voltage tracking control law is developed to ensure that all variables of the closed-loop system are semi-globally uniformly ultimately bounded. The tracking error of joint positions also converges to a small neighborhood around the origin such that the constraints on the joint angular positions and velocities are not transgressed during operation. Various scenarios for numerical simulations are given to show the potential of the proposed control algorithm when applied to a robot manipulator driven by permanent magnet dc motors.

Event-triggered adaptive fuzzy trajectory tracking control of unmanned surface vehicle with time-varying output constraints and input quantization.