Nearly data-based optimal control for linear discrete model-free systems with delays via reinforcement learning

Jilie Zhang, Huaguang Zhang, Binrui Wang and Tiaoyang Cai

International Journal of Systems Science, 2016, vol. 47, issue 7, 1563-1573

Abstract: In this paper, a nearly data-based optimal control scheme is proposed for linear discrete model-free systems with delays. The nearly optimal control can be obtained using only measured input/output data from systems, by reinforcement learning technology, which combines Q-learning with value iterative algorithm. First, we construct a state estimator by using the measured input/output data. Second, the quadratic functional is used to approximate the value function at each point in the state space, and the data-based control is designed by Q-learning method using the obtained state estimator. Then, the paper states the method, that is, how to solve the optimal inner kernel matrix P‾$\bar{P}$ in the least-square sense, by value iteration algorithm. Finally, the numerical examples are given to illustrate the effectiveness of our approach.

Date: 2016
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DOI: 10.1080/00207721.2014.941147

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