Title :
DE-based neural network nonlinear model predictive control and its application for the pH neutralization reactor control
Author :
Yu, Xiadong ; Huang, Dexian ; Wang, Xiong ; Jin, Yihui
Author_Institution :
Dept. of Autom., Tsinghua Univ., Beijing
Abstract :
In this paper, a nonlinear model predictive control (NMPC) algorithm based on differential evolution (DE) and radial base function (RBF) neural network is proposed. RBF neural network is used for the modeling. And DE algorithm is used to solve the optimal predictive control input due to its characteristic of global optimum, easy implementation and fast convergence. The simulation results on the pH control of the neutralization rector by the proposed DE-RBF-MPC show that this control strategy is effective.
Keywords :
nonlinear control systems; pH control; predictive control; radial basis function networks; differential evolution; neural network; nonlinear model predictive control; pH neutralization reactor control; radial base function; Inductors; Neural networks; Predictive control; Predictive models; Sampling methods; Trajectory; DE; MPC; RBF neural network; nonlinear system; pH neutralization;
Conference_Titel :
Control and Decision Conference, 2008. CCDC 2008. Chinese
Conference_Location :
Yantai, Shandong
Print_ISBN :
978-1-4244-1733-9
Electronic_ISBN :
978-1-4244-1734-6
DOI :
10.1109/CCDC.2008.4597587
