An LMI robust predictive control approach applied in a coupled tanks systems

This work deals of an on-line control strategy based on Robust Model Predictive Control (RMPC) technique applied in a real coupled tanks system. This process consists of two coupled tanks and a pump to feed the liquid to the system. The process variables (levels) are transmitted to the PLC (Programmable Logic Controller) thought a voltage signal. The control signal, in volts, generated in the PLC, is sent to the pump. The control objective (regulator problem) is to keep the tanks levels in the considered operation point even in disturbance presence. The RMPC is a technique that allows explicit incorporation of the plant uncertainty in the problem formulation. The goal is to design, at each time step, a state-feedback control law that minimizes a 'worst-case' infinite horizon objective function, subject to constraint in the control input. The existence of a feedback control law and satisfying the input constraints is reduced to a convex optimization over linear matrix inequalities (LMIs) problem. It is shown that for the plant uncertainty described by the polytope, the feasible receding horizon state feedback control design is robustly stabilizing. The software implementation of the RMPC is made using Scilab, and its communication with Coupled Tanks Systems is done through the OLE for Process Control (OPC) industrial protocol.