Implementation of Predictive Control Systems
This chapter presents three different implementation procedures for model predictive control systems. The first implementation is based on a microcontroller (low cost) for controlling a DC motor. In this application, the MATLAB® design programs, ‘mpc.m’ and associated functions, are utilized to calculate the predictive controller gain and the previous MATLAB closedloop simulation program is converted to a C program for real-time implementation on the micro-controller. The procedure is straightforward for those who understand C language. The second implementation is based on MATLAB Real-time Workshop and xPC target. This application is very useful for those who are not familiar with C language because the MATLAB Real-time Workshop and xPC target perform the conversion from MATLAB programs to C programs through the compilers in a systematic way. With these tools, we only need to create MATLAB embedded functions for the real-time applications. The third implementation uses the platform of a real-time PC-based supervisory control and data acquisition (SCADA) system. A pilot food extrusion plant is controlled by the continuous-time predictive controller developed in Chapter 6. In this application, the MATLAB program ‘cmpc.m’ is used to generate the predictive controller gain and the previous MATLAB closed-loop simulation program for a continuous-time system is converted to a C program for the real-time implementation.
KeywordsModel Predictive Control Screw Speed Motor Torque Programmable Logic Controller Laguerre Function
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