scholarly journals Double-layered nonlinear model predictive control based on Hammerstein–Wiener model with disturbance rejection

2018 ◽  
Vol 51 (7-8) ◽  
pp. 260-275 ◽  
Author(s):  
Hongbin Cai ◽  
Ping Li ◽  
Chengli Su ◽  
Jiangtao Cao
Keyword(s):  
Steady State ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Disturbance Rejection ◽  
Control Method ◽  
Dynamic Control ◽  
State Feedback Control ◽  
Nonlinear Model Predictive Control ◽  
Wiener Model

This paper presents the double-layered nonlinear model predictive control method for a continuously stirred tank reactor and a pH neutralization process that are subject to input disturbances and output disturbances at the same time. The nonlinear systems can be described as a Hammerstein -Wiener model. Furthermore, two nonlinear parts of the Hammerstein -Wiener model should be transformed into linear combination of known input and unknown disturbances, respectively. By taking advantage of Kalman filter, disturbances and states can be estimated. The estimated disturbances and states can be considered to calculate steady-state target in steady-state target calculation layer. Moreover, the state feedback control law can be obtained in dynamic control layer. A simple proof for offset-free control is given in the proposed method. The simulation results show that the controlled variable can achieve the offset-free control. It can be seen that the proposed method has better disturbance rejection performance, strong robustness and practical value.

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