Title :
A methodology for block-oriented industrial nonlinear system by nonlinear separation control with neural learning
Author :
Zhang, T. ; Nakamura, M.
Author_Institution :
Dept. of Adv. Syst. Control Eng., Saga Univ., Japan
Abstract :
This paper presents a general methodology for designing controller for block-oriented industrial nonlinear system by nonlinear separation control with neural learning. In this study, through rough approximation of inverse input output nonlinear statics and accurate compensation of nonlinear dynamics with rigid definition of neural network as well as learning from actual system, control performances can be improved. Based on this method, high-precision contour control of industrial articulated robot arm and outlet working fluid heat rate control of evaporator in energy conversion plant were realized. The experiment and simulation verified the significant potential of the proposed method to industrial nonlinear systems.
Keywords :
compensation; industrial control; learning (artificial intelligence); neurocontrollers; nonlinear control systems; nonlinear dynamical systems; block-oriented industrial nonlinear system; controller design; energy conversion plant evaporator; industrial articulated robot arm; industrial nonlinear systems; inverse I/O nonlinear statics; inverse input output nonlinear statics; neural learning; nonlinear separation control; outlet working fluid heat rate control; rough approximation; Control systems; Design methodology; Electrical equipment industry; Fluid dynamics; Industrial control; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Temperature control;
Conference_Titel :
SICE 2002. Proceedings of the 41st SICE Annual Conference
Print_ISBN :
0-7803-7631-5
DOI :
10.1109/SICE.2002.1195835