Abstract

FRANCISCO, M.  and  VEGA, P.. Automatic tuning of model predictive controllers based on multiobjective optimization. Lat. Am. appl. res. [online]. 2010, vol.40, n.3, pp.255-265. ISSN 0327-0793.

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_∞ norms of some closed loop transfer functions of the system, and others related to the range of manipulated and controlled variables, expressed using the l₁ 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.

Keywords : Model Predictive Control; Activated Sludge Process; Mixed Sensitivity Problem; Robust Control Theory; l₁ Norm.