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Implementation of Predictive Control Systems

Part of the Advances in Industrial Control book series (AIC)

Abstract

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.

Keywords

Model Predictive Control Screw Speed Motor Torque Programmable Logic Controller Laguerre Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer London 2009

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