Novel Discounted Adaptive Critic Control Designs With Accelerated Learning Formulation
Mingming Ha, Ding Wang, Derong Liu
Abstract
Inspired by the successive relaxation method, a novel discounted iterative adaptive dynamic programming framework is developed, in which the iterative value function sequence possesses an adjustable convergence rate. The different convergence properties of the value function sequence and the stability of the closed-loop systems under the new discounted value iteration (VI) are investigated. Based on the properties of the given VI scheme, an accelerated learning algorithm with convergence guarantee is presented. Moreover, the implementations of the new VI scheme and its accelerated learning design are elaborated, which involve value function approximation and policy improvement. A nonlinear fourth-order ball-and-beam balancing plant is used to verify the performance of the developed approaches. Compared with the traditional VI, the present discounted iterative adaptive critic designs greatly accelerate the convergence rate of the value function and reduce the computational cost simultaneously.