Robust nonlinear model predictive controller design based on multi-scenario formulation

Rui Huang, Lorenz T. Biegler

Abstract: We propose a robust nonlinear model predictive control (NMPC) algorithm based on multi-scenario nonlinear programming (NLP). We show that this multi-scenario formulation is Input-to-State practical stable (ISpS). Moreover, the proposed algorithm yields less steady-state offset than the minmax strategy.