Title :
The disturbance model in model based predictive control
Author :
Keyser, Robin De ; Ionescu, Clara Mihaela
Author_Institution :
Electr. Energy, Syst. & Autom. Dept., Ghent Univ., Belgium
Abstract :
Model based predictive control (MBPC) is a control methodology which uses a process model online in the control computer; this model is used for calculating output predictions and optimizing control actions. The importance of the system model has been generally recognized, but less attention has been paid to the role of the disturbance model. In this paper the importance of the disturbance model is indicated with respect to the EPSAC approach to MBPC. To illustrate this importance, an example of this advanced control methodology applied to a typical mechatronic system is presented, to compare the performances obtained by using different disturbance models. It clearly shows the benefits of using an "intelligent" disturbance model instead of the "default" model generally adopted in practice.
Keywords :
filtering theory; mechatronics; optimal control; optimisation; predictive control; real-time systems; three-term control; PID controller; control actions optimisation; disturbance filter; disturbance model; disturbance rejection; mechatronic system; model based predictive control; online process model; Automatic control; Automation; Cost function; Electrical equipment industry; Industrial control; Mechatronics; Optimal control; Predictive control; Predictive models; Process control;
Conference_Titel :
Control Applications, 2003. CCA 2003. Proceedings of 2003 IEEE Conference on
Print_ISBN :
0-7803-7729-X
DOI :
10.1109/CCA.2003.1223451