This paper concentrates upon the problem of adaptive neural finite-time tracking control for uncertain nonstrict-feedback nonlinear systems with input saturation. The design difficulty of non-smooth input saturation nonlinearity is solved by applying a smooth non-affine function to approximate the saturation signal. Neural networks, as a kind of specialized function estimators, are used to estimate the uncertain function. Meanwhile, a neural network-based observer is constructed to observe the unavailable states, and thus an observer-based adaptive finite-time tracking control strategy is developed by combining dynamic surface control (DSC) technique and backstepping approach. Furthermore, the stability of the considered system is analyzed via semi-global practical finite-time stability theory. Under the proposed control method, all the signals in the closed-loop system are bounded, and the system output can almost surely track the desired trajectory within a specified bounded error in a finite time. In the end, two examples are adopted to illustrate the validity of our results.

Observed-based Adaptive Finite-Time Tracking Control for a Class of Nonstrict-Feedback Nonlinear Systems with Input Saturation

Adaptive fault-tolerant control for switched nonlinear systems based on command filter technique

Event-triggered adaptive fuzzy output feedback control of MIMO switched nonlinear systems with average dwell time

This article studies a tracking control problem for a class of stochastic nonlinear systems with time-varying full-state constraints and asymmetric input saturation. Firstly, the Gauss Error Functi...

Command filter-based adaptive neural finite-time control for stochastic nonlinear systems with time-varying full-state constraints and asymmetric input saturation

In this paper, the issue of adaptive neural tracking control for uncertain switched multi-input multi-output (MIMO) nonstrict-feedback nonlinear systems with average dwell time is studied. The system under consideration includes unknown dead-zone inputs and output constraints. The uncertain nonlinear functions are identified via neural networks. Also, neural networks-based switched observer is constructed to approximate all unmeasurable states. By means of the information for dead-zone slopes and barrier Lyapunov function (BLF), the problems of dead-zone inputs and output constraints are tackled. Furthermore, dynamic surface control (DSC) scheme is employed to ensure that the computation burden is greatly reduced. Then, an observer-based adaptive neural control strategy is developed on the basis of backstepping technique and multiple Lyapunov functions approach. Under the designed controller, all the signals existing in switched closed-loop system are bounded, and system outputs can track the target trajectories within small bounded errors. Finally, the feasibility of the presented control algorithm is proved via simulation results.

Observer-based adaptive neural tracking control for output-constrained switched MIMO nonstrict-feedback nonlinear systems with unknown dead zone

Adaptive fuzzy output-feedback tracking control for switched stochastic pure-feedback nonlinear systems

Adaptive control design for uncertain switched nonstrict-feedback nonlinear systems to achieve asymptotic tracking performance

This article is devoted to designing an adaptive command filtered control scheme for a category of multiple‐input multiple‐output switched nonlinear systems with backlash‐like hysteresis. The “explosion of complexity” issue, which comes from the derivatives of the intermediate controller, is eliminated by using a command filter technique. Meanwhile, an error compensation mechanism is introduced to offset the filter error's impact on system performances and overcome the drawback of traditional dynamic surface control methods. Furthermore, a proper Lyapunov function and an adaptive law are fixed in the last step of the backstepping design procedure to surmount the design difficulty arising from the backlash‐like hysteresis nonlinearity. In a word, by utilizing the command filter technique, backstepping algorithm, and average dwell time method, an adaptive controller is constructed, which can not only guarantee that the system outputs track the desired trajectories as far as possible but also ensure all the signals are bounded. Simulation results are provided to illustrate the effectiveness of the proposed scheme.

Yuanqing Wang

Papers

Adaptive fuzzy output-feedback tracking control for switched stochastic pure-feedback nonlinear systems

Adaptive fault-tolerant control for switched nonlinear systems based on command filter technique

Adaptive control design for uncertain switched nonstrict-feedback nonlinear systems to achieve asymptotic tracking performance

Adaptive command filtered control for switched multi‐input multi‐output nonlinear systems with hysteresis inputs