Design of Robust Predictive Control Laws Using Set Membership Identified Models

Abstract

AbstractThis paper investigates the robust design of nonlinear model predictive control (NMPC) laws that employ approximated models, derived directly from process input‐output data. In particular, a nonlinear set membership (NSM) identification technique is used to obtain a system model and a bound of the related uncertainty. The latter is used to carry out a robust control design, via a min‐max formulation of the optimal control problem underlying the NMPC methodology. A numerical example with a nonlinear oscillator shows the effectiveness of the proposed approach.