Convergence analysis of GrDHP-based optimal control for discrete-time nonlinear system
Document Type
Conference Proceeding
Date of Original Version
10-31-2016
Abstract
Adaptive dynamic programming (ADP) has been investigated for its new architectures, algorithms and applications for years. Recently, the goal representation (Gr) design has been demonstrated with promising results to improve ADP control performance from certain perspectives. This paper is focused on the theoretical analysis of the goal representation dual heuristic dynamic programming (GrDHP). Starting from the general formulation of the GrDHP design, we provide the iterative algorithm for this method. The corresponding convergence analysis is showed in terms of the internal reinforcement signal, the performance index, and their derivatives. Our analysis assumes that the system is controllable and stabilizable. Then, neural-network-based implementation of this method is presented. Simulation study validates the theoretical analysis of this paper and also shows the effectiveness of the GrDHP method.
Publication Title, e.g., Journal
Proceedings of the International Joint Conference on Neural Networks
Volume
2016-October
Citation/Publisher Attribution
Zhong, Xiangnan, Zhen Ni, and Haibo He. "Convergence analysis of GrDHP-based optimal control for discrete-time nonlinear system." Proceedings of the International Joint Conference on Neural Networks 2016-October, (2016): 4557-4564. doi: 10.1109/IJCNN.2016.7727797.