Fuzzy-Based Goal Representation Adaptive Dynamic Programming

Abstract

In this paper, a novel nonlinear learning controller called fuzzy-based goal representation adaptive dynamic programming (Fuzzy-GrADP) is proposed. In the proposed GrADP method, a goal representation network is introduced to generate an adaptive internal reinforcement signal to the critic network to help the controller provide a general mapping between the input and output actions. Moreover, in the proposed architecture, the action network in the GrADP is improved by using the fuzzy hyperbolic model, which combines the merits of the fuzzy model and the neural network model. Based on the back-propagation technique, the parameters in the membership functions and the fuzzy rules are all undergo training and online adapting. The proposed controller is tested on two numerical benchmarks, and the simulation results show that the proposed controller outperforms the original adaptive dynamic fuzzy controller and the pure neural network-based GrADP controller. In addition, the proposed controller is further applied on a large multimachine power system for static var compensator damping control, where simulation results demonstrate the effectiveness of the proposed approach on real applications. Furthermore, in order to demonstrate the theoretical guarantee of the proposed method, Lyapunov stability analysis to support the proposed Fuzzy-GrADP approach has also been carried out.