A model based predictive control scheme for nonlinear process

Author

Wang, Jin ; Thomas, Garth

Author_Institution

Dept. of Chem. Eng., West Virginia Univ., Montgomery, WV

fYear

2006

fDate

14-16 June 2006

Abstract

A model based predictive control (MPC) strategy for nonlinear process systems is presented. The sensitivity between the controlled system input and output is identified in the implementation of this strategy. The comparisons of MPC, gain scheduling control and conventional PID control highlights their consistency as well as differences, and the advantages of the adaptive controller. A decomposed neural network (DNN) model is applied to the MPC scheme. Stability analysis of the MPC system is presented based on Lyapunov theory. From the stability investigation, the stability condition for the DNN-based control system is obtained. Benchmark example results show that the proposed MPC method can effectively control unknown nonlinear systems

Keywords

Lyapunov methods; control system synthesis; model reference adaptive control systems; neurocontrollers; nonlinear control systems; predictive control; stability; three-term control; Lyapunov theory; PID control; adaptive controller; control system; decomposed neural network model; gain scheduling control; model based predictive control; nonlinear process systems; stability; Adaptive control; Control systems; Neural networks; Nonlinear control systems; Nonlinear systems; Predictive control; Predictive models; Programmable control; Stability analysis; Three-term control;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 2006

Conference_Location

Minneapolis, MN

Print_ISBN

1-4244-0209-3

Electronic_ISBN

1-4244-0209-3

Type

conf

DOI

10.1109/ACC.2006.1657487

Filename

1657487