Journal
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
Volume 47, Issue 7, Pages 1563-1573Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207721.2014.941147
Keywords
data-based optimal control; linear discrete time-delay system; model-free system; Q-learning; value iteration; reinforcement learning
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Funding
- National Natural Science Foundation of China [61034005, 61203046]
- National High Technology Research and Development Program of China [2012AA040104]
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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) over bar in the least-square sense, by value iteration algorithm. Finally, the numerical examples are given to illustrate the effectiveness of our approach.
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