Adaptive Neural Tracking Control for Nonlinear Time-Delay Systems With Full State Constraints
针对同时存在全状态约束和时滞的不确定非线性系统，提出一种自适应神经跟踪控制策略，利用障碍李雅普诺夫函数和反步法确保状态约束不被违反，并消除时滞影响，仿真验证了有效性。
In this paper, an adaptive neural tracking control strategy is presented to stabilize a class of uncertain nonlinear strict-feedback systems with the full state constraints and time-delays. Because the full state constraints and time-delays appear simultaneously in the systems, they lead to the difficulties in the controller design. The opportune barrier Lyapunov functions (BLFs) are designed to ensure that the states constraints are not violated. The novel backstepping procedures with BLFs are utilized to eliminate the effect of the nonlinear system which caused by the time-delays. Finally, it is proved that all the signals in the closed-loop system are semiglobal uniformly ultimately bounded and the tracking errors converge to a small interval based on proposed Lyapunov and backstepping design method. The effectiveness of the proposed scheme is demonstrated by a simulation in this paper.