Title :
Optimal learning control for discrete-time nonlinear systems using generalized policy iteration based adaptive dynamic programming
Author :
Qinglai Wei ; Derong Liu
Author_Institution :
State Key Lab. of Manage. & Control for Complex Syst., Inst. of Autom., Beijing, China
Abstract :
In this paper, a novel generalized policy iteration algorithm is investigated to solve infinite horizon optimal control problems for discrete-time nonlinear systems. Two iteration indices are introduced in the generalized policy iteration algorithm, which iterate for policy improvement and policy evaluation, respectively. For the first time the properties of monotonicity, convergence and admissibility for the generalized policy iteration algorithm are analyzed to guarantee that the iterative performance index function converges to the optimum and the iterative control law stabilizes the control system. Finally, numerical results are presented to illustrate the performance of the developed method.
Keywords :
discrete time systems; dynamic programming; iterative methods; nonlinear control systems; optimal control; adaptive dynamic programming; discrete-time nonlinear systems; generalized policy iteration algorithm; infinite horizon optimal control problems; iteration indices; iterative control law; iterative performance index function; optimal learning control; policy evaluation; policy improvement; Adaptive critic designs; adaptive dynamic programming; approximate dynamic programming; generalized policy iteration; neural networks; neuro-dynamic programming; nonlinear systems; optimal control; reinforcement learning;
Conference_Titel :
Intelligent Control and Automation (WCICA), 2014 11th World Congress on
DOI :
10.1109/WCICA.2014.7052990
