Title :
Neurofuzzy model based l∞ predictive control of nonlinear CSTR system
Author :
Wu, Q. ; Wang, Y.J. ; Zhu, Q.M. ; Warwick, K.
Author_Institution :
Dept. of Control Sci. & Eng., Huazhong Univ. of Sci. & Technol., Wuhan, China
Abstract :
In this paper the nonlinear dynamics of a continuously stirred tank reactor (CSTR) are modelled with a neuro-fuzzy network, so that a predictive control strategy is developed based on the l∞ norm performance. Stability of the closed loop system is proved that the system is stable if each local linear control system is closed loop stable. The pH control in neutralisation process within the CSTR was simulated to indicate that the control performance is superior to that from quadratic predictive control.
Keywords :
chemical industry; closed loop systems; fuzzy neural nets; nonlinear control systems; optimal control; pH control; predictive control; process control; stability; CSTR system; closed loop system; continuously stirred tank reactor; fuzzy neural network; neutralisation; nonlinear system; pH control; predictive control; stability; Closed loop systems; Continuous-stirred tank reactor; Control systems; Fuzzy neural networks; Inductors; Nonlinear dynamical systems; Predictive control; Predictive models; Process control; Stability;
Conference_Titel :
Control Applications, 2002. Proceedings of the 2002 International Conference on
Print_ISBN :
0-7803-7386-3
DOI :
10.1109/CCA.2002.1040160
