Data-driven adaptive optimal control of linear uncertain systems with unknown jumping dynamics |
| |
| Authors: | Meng Zhang Ming-Gang Gan Jie Chen Zhong-Ping Jiang |
| |
| Institution: | 1. School of Automation, Beijing Institute of Technology, Beijing 100081, PR China;2. Department of Electrical and Computer Engineering, Tandon School of Engineering, New York University, Brooklyn, NY 11201, USA |
| |
| Abstract: | This paper focuses on the optimal control of a DC torque motor servo system which represents a class of continuous-time linear uncertain systems with unknown jumping internal dynamics. A data-driven adaptive optimal control strategy based on the integration of adaptive dynamic programming (ADP) and switching control is presented to minimize a predefined cost function. This takes the first step to develop switching ADP methods and extend the application of ADP to time-varying systems. Moreover, an analytical method to give the initial stabilizing controller for policy iteration ADP is proposed. It is shown that under the proposed adaptive optimal control law, the closed-loop switched system is asymptotically stable at the origin. The effectiveness of the strategy is validated via simulations on the DC motor system model. |
| |
| Keywords: | Corresponding author |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
