Robust offset‐free nonlinear model predictive control for systems learned by neural nonlinear autoregressive exogenous models

Abstract

This paper presents a robust model predictive control (MPC) scheme that provides offset-free setpoint tracking for systems described by neural nonlinear autoregressive exogenous (NNARX) models. To this end, a NNARX model that learns the dynamics of the plant from input-output data is augmented with an explicit integral action on the output tracking error. A robust tube-based MPC is finally designed, leveraging the unique structure of the model, to ensure robust offset-free convergence to constant reference signals even in case of plant-model mismatch. Numerical simulations on a water heating system show the effectiveness of the proposed control algorithm.