Adaptive Dynamic Programming Algorithm for Uncertain Nonlinear Switched Systems

Phuong Nam Dao, Hong Quang Nguyen, Nhat Tung Nguyen


In this work, an adaptive dynamic programming algorithm is proposed for a class of nonlinear switched systems with external disturbances. The neural networks are employed to approximate the part of actor as well as critic. The training technique is investigated based on the minimization of square error Hamilton function. Stability analysis shows that all the closed-loop system signals are uniformly ultimately bounded (UUB). The simulation results validate the effectiveness of the proposed controller.


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