This article is concerned with the issue of adaptive prescribed performance tracking control for a class of pure-feedback nonlinear systems with full-state time-varying constraints. By using the hyperbolic tangent function as nonlinear mapping technique, the obstacle of the full-state constraints is resolved. A novel adaptive control method is proposed by prescribed performance control with command filtered backstepping design, and the insufficient of the dynamic surface method is conquered by the error compensation mechanism. Radial basis function neural networks are utilized to approximate the unknown nonlinear functions. Based on the Lyapunov stability theory, it is shown that all signals in the closed-loop system are semiglobal uniformly ultimately bounded and the output tracking error always converges to the prescribed performance bound.Two simulation examples demonstrate the capability of the proposed control strategy.

The finite-time command filter tracking control for a class of nonstrictly feedback nonlinear systems with unmodeled dynamics and full-state constraints is investigated in this paper. The hyperbolic tangent function is used as a nonlinear mapping technique to solve the obstacle of the full-state constraints. A new adaptive finite time control method is proposed through command filtering reverse engineering, and the shortcomings of the dynamic surface control (DSC) method are overcome by the error compensation mechanism. Dynamic signal is designed to handle dynamical uncertain terms. Normalization signal is designed to handle input unmodeled dynamics. Unknown nonlinear functions are approximated by radial basis function neural networks. Based on the Lyapunov stability theory, it is proved that all signals in the closed-loop system are semi-globally consistent and finally bounded and the output tracking error converges in finite time. Two numerical examples are utilized to verify the effectiveness of the proposed control approach.

SummaryThis article is concerned with the finite‐time adaptive event‐triggered control for stochastic nonstrict‐feedback nonlinear systems, which are under asymmetric full‐state time‐varying constraints. By utilizing the hyperbolic tangent function as a nonlinear mapping approach, the system with time‐varying constraints is transformed into an unconstrained stochastic nonlinear system. To reduce the communication burden and energy consumption, adaptive event‐triggered control (ETC) is extended to the converted stochastic nonstrict‐feedback system combining the dynamic surface control (DSC) technology and the characteristic of Gaussian function. The designed controller can make the system semi‐globally finite‐time stable in probability (SGFSP), which ensures a fast convergence speed. Moreover, to approximate the unknown nonlinear functions, radial basis function neural networks (RBF NNs) are adopted. The proposed control scheme can be confirmed in the fact that all signals in the closed‐loop system are semi‐globally uniformly bounded in probability, and the tracking error converges rapidly to a small neighborhood of zero in finite time. Two simulation results are given to demonstrate the effectiveness of the proposed controller.

Summary This article is concerned with the issue of adaptive event‐triggered control for a class of pure‐feedback multi‐input multi‐output (MIMO) nonlinear systems with full‐state time‐varying constraints. By using the hyperbolic tangent as nonlinear mapping technique, the uncertain constrained MIMO non‐affine system is changed into a novel unconstrained MIMO system. Dynamic surface control (DSC) strategy is used to solve the issue of “explosion of complexity.” Command filter is adopted to overcome the insufficient of the DSC method by the error compensation mechanism. Adaptive event‐triggered control scheme is developed for the transformed non‐affine system based on relative threshold mechanism. Radial basis function neural networks are utilized to approximate the unknown nonlinear functions. All the signals in the controlled system are proved to be semi‐globally uniformly ultimately bounded by adding to compensation signals into the whole Lyapunov function. Two simulation examples demonstrate the capability of the proposed control strategy.