Abstract
In this paper, an iterative approximate dynamic programming algorithm is proposed for solving the approximate optimal tracking control problem for a class of nonlinear discrete-time switched systems. Firstly, the optimal tracking problem is converted into designing an optimal regulator for the tracking error dynamics. And then, the iterative approximate dynamic programming algorithm is proposed to obtain the approximate solution of the Hamilton-Jacobi-Bellman (HJB) equation. Next, two neural networks are used to approximate the iterative cost function and the iterative control law, respectively. Finally, simulation results are given to verify the effectiveness of the proposed algorithm.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
