Data-driven adaptive optimal control of linear uncertain systems with unknown jumping dynamics. Issue 12 (August 2019)

• Record Type: Journal Article
• Authors: Zhang, Meng; Gan, Ming-Gang; Chen, Jie; Jiang, Zhong-Ping
• Is Part Of: Journal of the Franklin Institute. Volume 356:Issue 12(2019)
• Page Start: 6087
• Page End: 6105
• Publication Date: 2019-08
• Subjects: Science -- Periodicals; Technology -- Periodicals; Patents -- United States -- Periodicals; 505
• DOI: 10.1016/j.jfranklin.2019.05.020
• Languages: English
• ISSNs: 0016-0032

Abstract

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.