Article: Lu, WJ; Zhu, PP; Ferrari, S; “A Hybrid-Adaptive Dynamic Programming Approach for the Model-Free Control of Nonlinear Switched Systems”, IEEE Transactions on Automatic Control, 61(10):3203-3208
Abstract: This paper presents a hybrid adaptive dynamic programming (hybrid-ADP) approach for determining the optimal continuous and discrete control laws of a switched system online, solely from state observations. The new hybrid-ADP recurrence relationships presented are applicable to model-free control of switched hybrid systems that are possibly nonlinear. The computational complexity and convergence of the hybrid-ADP approach are analyzed, and the method is validated numerically showing that the optimal controller and value function can be learned iteratively online from state observations.
Funding Acknowledgement: National Science Foundation [ECCS 1028506, 1556900]
Funding Text: This work was supported by the National Science Foundation under Grants ECCS 1028506 and 1556900. Recommended by Associate Editor Z. Chen.