Adaptive Event-Triggered Near-Optimal Tracking Control for Unknown Continuous-Time Nonlinear Systems
Adaptive Event-Triggered Near-Optimal Tracking Control for Unknown Continuous-Time Nonlinear Systems
复制标题
DOI：
10.1109/access.2021.3140076
复制
发表时间：
2022-01-01
期刊：
影响因子：
3.9
通讯作者：
Zhou, Tianmin
中科院分区：
文献类型：
--
作者：
Wang, Kunfu;Gu, Qijia;Zhou, Tianmin
本文研究了连续时间（CT）未知非线性系统的事件触发最优跟踪控制（ETOTC）问题。为了解决ETOTC问题，使用由误差系统动力学和参考动力学组成的增强系统引入新的折扣性能指数函数（DPIF）。开发了一种新颖的事件触发（ET）自适应动态规划（ADP）方法来求解 ET Hamilton-Jacobi-Bellman 方程（HJBE）。该方法通过标识符-批评架构实现，该架构由两个神经网络（NN）组成：标识符 NN 用于估计未知系统动力学，构造批评 NN 以获得 ET HJBE 的近似解。通过Lyapunov直接法证明了增广闭环系统和临界估计误差最终一致有界（UUB）。最后，两个模拟说明了所开发方法的有效性。
This paper studies the event-triggered optimal tracking control (ETOTC) problem of continuous-time (CT) unknown nonlinear systems. In order to solve the ETOTC problem, an augmented system composed of the error system dynamics and the reference dynamics is used to introduce a new discounted performance index function (DPIF). A novel event-triggered (ET) adaptive dynamic programming (ADP) method is developed to solve the ET Hamilton-Jacobi-Bellman equation (HJBE). The presented method is implemented via an identifier-critic architecture, which consists of two neural networks (NNs): an identifier NN is applied to estimate the unknown system dynamics, and a critic NN is constructed to obtain the approximate solution of the ET HJBE. The augmented closed-loop system and the critic estimation error are proved to be ultimately uniformly bounded (UUB) by the Lyapunov direct method. Finally, two simulations illustrate the effectiveness of the developed method.