Data-driven adaptive optimal control of linear uncertain systems with unknown jumping dynamics

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...

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Bibliographic Details
Published in:Journal of the Franklin Institute 2019-08, Vol.356 (12), p.6087-6105
Main Authors: Zhang, Meng, Gan, Ming-Gang, Chen, Jie, Jiang, Zhong-Ping
Format: Article
Language:English
Subjects:
Adaptive control
Closed loop systems
Computer simulation
Control systems
Control theory
Controllers
D C motors
Dynamic programming
Electric currents
Electric motors
Iterative methods
Optimal control
Servomotors
Switching
Torque motors
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Summary: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.
ISSN:0016-0032
1879-2693
0016-0032
DOI:10.1016/j.jfranklin.2019.05.020