Controlling Nonlinear Processes, using Laguerre Functions Based Adaptive Model Predictive Control (AMPC) Algorithm

Abstract

Laguerre function has many advantages such as good approximation capability for different systems, low computational complexity and the facility of on-line parameter identification. Therefore, it is widely adopted for complex industrial process control. In this work, Laguerre function based adaptive model predictive control algorithm (AMPC) was implemented to control continuous stirred tank reactor (CSTR) process temperature runaways. Simulation result reveals that AMPC has a good performance in set-point tracking and load rejection. For comparison, a nonlinear model predictive control based on Laguerre- wiener model was also applied to the process. Simulation result demonstrates that the two controllers have the same performance in set point tracking and load rejection problem.