A model-free deep integral policy iteration structure for robust control of uncertain systems

Abstract

In this paper, we develop an improved data-based integral policy iteration method to address the robust control issue for nonlinear systems. Combining multi-step neural networks with pre-training, the condition of selecting the initial admissible control policy is relaxed even though the information of system dynamics is unknown. Based on adaptive critic learning, the established algorithm is conducted to attain the optimal controller. Then, the robust control strategy is derived by adding the feedback gain. Furthermore, the computing error is considered during the process of implementing matrix inverse operation. Finally, two examples are presented to verify the effectiveness of the constructed algorithm.

Keywords:

• Adaptive critic learning
• integral policy iteration
• multi-step neural networks
• robust control
• uncertain systems

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data sharing is not applicable to this article as no new data were created or analysed in this study.

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China [grant numbers 62222301, 61890930-5, and 62021003]; in part by the National Key Research and Development Program of China [grant numbers 2021ZD0112302, 2021ZD0112301, and 2018YFC1900800-5]; and in part by the Beijing Natural Science Foundation [grant number JQ19013].