Adaptive dynamic programming for optimal control of unknown nonlinear discrete-time systems

Author

Liu, Derong ; Wang, Ding ; Zhao, Dongbin

Author_Institution

Key Lab. of Complex Syst. & Intell. Sci., Chinese Acad. of Sci., Beijing, China

fYear

2011

fDate

11-15 April 2011

Firstpage

242

Lastpage

249

Abstract

An intelligent optimal control scheme for unknown nonlinear discrete-time systems with discount factor in the cost function is proposed in this paper. An iterative adaptive dynamic programming (ADP) algorithm via globalized dual heuristic programming (GDHP) technique is developed to obtain the optimal controller with convergence analysis. Three neural networks are used as parametric structures to facilitate the implementation of the iterative algorithm, which will approximate at each iteration the cost function, the optimal control law, and the unknown nonlinear system, respectively. Two simulation examples are provided to verify the effectiveness of the presented optimal control approach.

Keywords

convergence; discrete time systems; dynamic programming; iterative methods; neurocontrollers; nonlinear control systems; optimal control; GDHP; convergence analysis; cost function; globalized dual heuristic programming; intelligent optimal control; iterative adaptive dynamic programming; iterative algorithm; neural networks; unknown nonlinear discrete-time systems; Artificial neural networks; Integrated optics; Neurons; Optimal control; Riccati equations; Adaptive critic designs; adaptive dynamic programming; approximate dynamic programming; globalized dual heuristic programming; intelligent control; neural dynamic programming; neural networks; optimal control;

fLanguage

English

Publisher

ieee

Conference_Titel

Adaptive Dynamic Programming And Reinforcement Learning (ADPRL), 2011 IEEE Symposium on

Conference_Location

Paris

Print_ISBN

978-1-4244-9887-1

Type

conf

DOI

10.1109/ADPRL.2011.5967357

Filename

5967357