Adaptive recurrent neural network control using a structure adaptation algorithm |
| |
Authors: | Chun-Fei Hsu |
| |
Affiliation: | (1) Department of Electrical Engineering, Chung Hua University, Hsinchu, 300, Taiwan, ROC |
| |
Abstract: | This paper proposes an adaptive recurrent neural network control (ARNNC) system with structure adaptation algorithm for the
uncertain nonlinear systems. The developed ARNNC system is composed of a neural controller and a robust controller. The neural
controller which uses a self-structuring recurrent neural network (SRNN) is the principal controller, and the robust controller
is designed to achieve L
2 tracking performance with desired attenuation level. The SRNN approximator is used to online estimate an ideal tracking controller
with the online structuring and parameter learning algorithms. The structure learning possesses the ability of both adding
and pruning hidden neurons, and the parameter learning adjusts the interconnection weights of neural network to achieve favorable
approximation performance. And, by the L
2 control design technique, the worst effect of approximation error on the tracking error can be attenuated to be less or equal
to a specified level. Finally, the proposed ARNNC system with structure adaptation algorithm is applied to control two nonlinear
dynamic systems. Simulation results prove that the proposed ARNNC system with structure adaptation algorithm can achieve favorable
tracking performance even unknown the control system dynamics function. |
| |
Keywords: | Adaptive control Robust control Recurrent neural network Structure adaptation |
本文献已被 SpringerLink 等数据库收录! |
|