Convergence analysis of GrDHP-based optimal control for discrete-time nonlinear system

Document Type

Conference Proceeding

Date of Original Version



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

Proceedings of the International Joint Conference on Neural Networks