Research on Simplified Design of Model Predictive Control

Abstract

PID controllers have been dominant in the field of process control for a long time, but their control quality is not ideal and the difficulty of parameter tuning has always been a problem. MPCs have good control quality and robustness, but due to the complexity of the algorithm, most are limited to software on PC machines. Although there are examples of implementations on hardware, they are restricted to specific scenarios and are of an experimental nature. The barriers to application and maintenance are high, and therefore, it has not become as popular as PID. The common self-balancing industrial objects are approximated as a first order plus dead time (FOPDT) model, and various parameters are simplified to obtain the control law of the simplified MPC controller. The control law has a small amount of calculation, good control quality, simple parameter settings, and is suitable for embedding in the field controller. Coupled with the auxiliary identification method, field technicians can easily use it. MATLAB (2016a) comparative simulation experiments show that the simplified MPC controller has obvious control advantages over PID. The results of field engineering applications also show that the simplified MPC controller can feasibly replace the PID algorithm in industrialization.

Keywords

• model predictive control
• parameter tuning
• identification
• simple design
• process control