Robust controller design of continuous-time nonlinear system using neural network
Document Type
Conference Proceeding
Date of Original Version
12-1-2013
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. © 2013 IEEE.
Publication Title, e.g., Journal
Proceedings of the International Joint Conference on Neural Networks
Citation/Publisher Attribution
Zhong, Xiangnan, Haibo He, and Danil V. Prokhorov. "Robust controller design of continuous-time nonlinear system using neural network." Proceedings of the International Joint Conference on Neural Networks (2013). doi: 10.1109/IJCNN.2013.6707098.