DocumentCode :
671756
Title :
Robust controller design of continuous-time nonlinear system using neural network
Author :
Xiangnan Zhong ; Haibo He ; Prokhorov, Danil V.
Author_Institution :
Dept. of Electr., Comput. & Biomed. Eng., Univ. of Rhode Island, Kingston, RI, USA
fYear :
2013
fDate :
4-9 Aug. 2013
Firstpage :
1
Lastpage :
8
Abstract :
In this paper, we propose an optimal control method based on the solution of Hamilton-Jacobi-Bellman (HJB) equation for the continuous-time nonlinear system with bounded unknown perturbation. The robust control system is converted into the corresponding optimal control system with appropriate performance index and the equivalence of the transformation is proved, i.e., the solution of the optimal control problem can globally asymptotically stabilize the robust control system. Adaptive dynamic programming (ADP) based approach is presented to iteratively approximate the optimal performance index and obtain the optimal control policy. A neural network with adaptive weights is applied to implement this approach. An example is given to illustrate the proposed method.
Keywords :
asymptotic stability; continuous time systems; control system synthesis; dynamic programming; neurocontrollers; nonlinear control systems; optimal control; robust control; ADP; HJB; Hamilton-Jacobi-Bellman equation; adaptive dynamic programming based approach; adaptive weights; asymptotic stability; continuous-time nonlinear system; neural network; optimal control method; optimal control system; optimal performance index; performance index; robust control system; robust controller design; transformation equivalence; Equations; Neural networks; Nonlinear systems; Optimal control; Performance analysis; Robust control; Uncertainty;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), The 2013 International Joint Conference on
Conference_Location :
Dallas, TX
ISSN :
2161-4393
Print_ISBN :
978-1-4673-6128-6
Type :
conf
DOI :
10.1109/IJCNN.2013.6707098
Filename :
6707098
Link To Document :
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