Title :
Neural network adaptive control for a class of nonlinear systems based on hyperstability
Author :
Xiaohe, Liu ; Hang, Su ; Lihua, Liu ; Yaohui, Zhang
Author_Institution :
Sch. of Autom., Beijing Inf. Sci. & Technol. Univ., Beijing
Abstract :
This paper discussed the neural network in the application of nonlinear system adaptive control. For a typical class of nonlinear system, A BP neural network is designed to control the system based on hyperstability. The nonlinear link was eliminated by introducing its inverse model, for transforming the nonlinearity into a linear system. The neural network controllerpsilas purpose is to optimize the effect on tracing velocity and make the output of controlled object closing to the one of reference model. Satisfactory results have been achieved by using MATLAB simulation on the model for three-phase electrode regulator system of arc furnace. The simulation and analysis shows that neural network adaptive control system is more effective than the traditional PID system, and guarantees the system has a better dynamic quality.
Keywords :
arc furnaces; backpropagation; control nonlinearities; control system analysis computing; linear systems; mathematics computing; model reference adaptive control systems; neurocontrollers; nonlinear control systems; stability; three-term control; BP neural network; MATLAB simulation; adaptive control; hyperstability; inverse model; linear system; model-reference adaptive control; nonlinear systems; three-phase electrode regulator system; traditional PID system; Adaptive control; Control systems; Inverse problems; Linear systems; MATLAB; Mathematical model; Neural networks; Nonlinear control systems; Nonlinear systems; Velocity control; Inverse model; Model-reference adaptive control; Neural network; Nonlinear system;
Conference_Titel :
Control Conference, 2008. CCC 2008. 27th Chinese
Conference_Location :
Kunming
Print_ISBN :
978-7-900719-70-6
Electronic_ISBN :
978-7-900719-70-6
DOI :
10.1109/CHICC.2008.4605307