Stable Model Predictive Control tuning considering asymmetric bounded signals

In this paper the consideration of asymmetric bounded signals (disturbances, inputs) in a general procedure for tuning infinite horizon model predictive controllers (MPC) with constraints is presented. These measures are stated by means of an asymmetric objective functional, instead of the standard l1 norm. The MPC is implemented with a terminal penalty to guarantee stability for all tuning parameters. Moreover, multiple models have been considered to ensure robust performance of the closed loop process, in the face of high non linear dynamics and load disturbances. The methodology has been applied to tune an MPC for the activated sludge process in a simulated wastewater treatment plant (WWTP). The problem is stated as a non linear programming problem with constraints and solved by Sequential Quadratic Programming (SQP).