EXTENDED MPC FOR CLOSED-LOOP RE-IDENTIFICATION BASED ON PROBABILISTIC INVARIANT SETS

ALEJANDRO ANDERSON; ALEJANDRO GONZÁLEZ; ANTONIO FERRAMOSCA; ERNESTO KOFMAN

Buenos Aires

Congreso; 25 Congreso Argentino de Control Automático; 2016

AADECA

Recently, a Model Predictive Control (MPC) scheme suitable for closed-loop re-identification was proposed which solves, in a non-conservative form, the potential conflict between the persistent excitation of the system and the stabilization. The scheme uses the concept of probabilistic invariance to define the target set, exploiting in that way the knowledge of the probabilistic distribution of the excitation signal to design a non-competitive two-objective MPC formulation. In this work, we prove some theoretical properties of the scheme that have fundamental practical consequences, including the finite--time convergence to the target set and a lower probability bound about the period of time the state remains in that set for the identification procedure. We also include new simulation results comparing the performance of the proposed approach with those of a previous deterministic formulation.