AUTOMATIC TUNING OF MODEL PREDICTIVE CONTROLLERS BASED ON MULTIOBJECTIVE OPTIMIZATION

In this work a general procedure for tuning multivariable model predictive controllers (MPC) with constraints is presented. Control system parameters are obtained by solving a multiobjective optimization problem. The set of objectives includes controllability aspects, in terms of the H-infinity norms of some closed loop transfer functions of the system, and others related to the range of manipulated and controlled variables, expressed using the l(1), norm. Moreover, the use of multiple linearized models for tuning, allows for the specification of robust performance criteria through a set of constraints. The mathematical optimization for tuning all controller parameters is tackled in two iterative steps. First, integer parameters are obtained using a specific random search, and secondly a sequential programming based method is used to tune the real parameters. As a validation example, the tuning of the control system for the activated sludge process of a wastewater treatment plant has been selected.