Title :
A novel non-linear model predictive controller based on minimal resource allocation network and its application in CSTR PH process
Author :
Haichuan, Lou ; Wenzhan, Dai
Author_Institution :
Dept. of Autom. Control, Zhejiang Sci-Tech Univ., Hangzhou
Abstract :
In this paper, a novel predictive controller based on minimal resource allocation network for non-linear system is presented. The controller combines the advantages of MRAN and neural predictor. The implemented neural predictive controller not only effectively eliminates the most significant obstacles but also be very robustness. At last, the algorithm is applied in a high nonlinear continuous stirred tank reactor (CSTR) pH process model and presents a better real-time control effect.
Keywords :
chemical reactors; neurocontrollers; nonlinear control systems; pH control; predictive control; resource allocation; minimal resource allocation network; neural predictor; nonlinear continuous stirred tank reactor; nonlinear model predictive controller; nonlinear system; pH process model; Automatic control; Continuous-stirred tank reactor; Neural networks; Neurons; Nonlinear control systems; Prediction algorithms; Predictive control; Predictive models; Resource management; Robust control; CSTR pH process model; Minimal Resource Allocation network; Model predictive control; Non-linear system;
Conference_Titel :
Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4244-2113-8
Electronic_ISBN :
978-1-4244-2114-5
DOI :
10.1109/WCICA.2008.4593855
