期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 47, 期 10, 页码 3367-3379出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2016.2586082
关键词
Adaptive critic designs; adaptive dynamic programming (ADP); approximate dynamic programming; local policy iteration; neuro-dynamic programming; nonlinear systems; optimal control
类别
资金
- National Natural Science Foundation of China [61233001, 61273140, 61374105, 61503379, 61533017, 61304079, U1501251]
In this paper, a discrete-time optimal control scheme is developed via a novel local policy iteration adaptive dynamic programming algorithm. In the discrete-time local policy iteration algorithm, the iterative value function and iterative control law can be updated in a subset of the state space, where the computational burden is relaxed compared with the traditional policy iteration algorithm. Convergence properties of the local policy iteration algorithm are presented to show that the iterative value function is monotonically nonincreasing and converges to the optimum under some mild conditions. The admissibility of the iterative control law is proven, which shows that the control system can be stabilized under any of the iterative control laws, even if the iterative control law is updated in a subset of the state space. Finally, two simulation examples are given to illustrate the performance of the developed method.
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