DocumentCode :
1799346
Title :
An adaptive dynamic programming algorithm to solve optimal control of uncertain nonlinear systems
Author :
Xiaohong Cui ; Yanhong Luo ; Huaguang Zhang
Author_Institution :
Sch. of Inf. Sci. & Eng., Northeastern Univ., Shenyang, China
fYear :
2014
fDate :
9-12 Dec. 2014
Firstpage :
1
Lastpage :
6
Abstract :
In this paper, an approximate optimal control method based on adaptive dynamic programming(ADP) is discussed for completely unknown nonlinear system. An online critic-action-identifier algorithm is developed using neural network systems, where the criticaction networks approximate the optimal value function and optimal control and the other two neural networks approximates the unknown system. Furthermore the adaptive tuning laws are given based on Lyapunov approach, which ensures the uniform ultimate bounded stability of the closed-loop system. Finally, the effectiveness is demonstrated by a simulation example.
Keywords :
Lyapunov methods; adaptive control; closed loop systems; dynamic programming; function approximation; neurocontrollers; nonlinear control systems; optimal control; stability; uncertain systems; ADP; Lyapunov approach; adaptive dynamic programming algorithm; adaptive tuning laws; approximate optimal control method; closed-loop system; criticaction networks; neural network systems; online critic-action-identifier algorithm; optimal value function approximation; ultimate bounded stability; uncertain nonlinear systems; Artificial neural networks; Equations; Function approximation; Heuristic algorithms; Mathematical model; Optimal control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), 2014 IEEE Symposium on
Conference_Location :
Orlando, FL
Type :
conf
DOI :
10.1109/ADPRL.2014.7010643
Filename :
7010643
Link To Document :
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