Min-Max predictive control of a pilot plant using a QP approach

— The practical implementation of Min-Max MPC (MMMPC) controllers is limited by the computational burden required to compute the control law. This problem can be circumvented by using approximate solutions or upper bounds of the worst possible case of the performance index. In a previous work, the authors presented a computationally efficient MMMPC control strategy in which a close approximation of the solution of the min-max problem is computed using a quadratic programming problem. In this paper, this approach is validated through its application to a pilot plant in which the temperature of a reactor is controlled. The pilot plant is operated with a Simatic IT SCADA system. The controller has been implemented in Matlab and connected to the SCADA using OPC. Realistic values of the parameters of the MMMPC controller have been used. The behavior of the system and the controller is illustrated by means of experimental results.

• Type: Conference
• Year: 2008
• Where: CDC
• Authors: Jorn Klaas Gruber, Daniel R. Ramírez, Teodoro Alamo, Carlos Bordons, Eduardo F. Camacho
• Keywords: CDC 2008, Control Systems, Pilot Plant, Quadratic Programming Problem, Worst Possible Case