Robust decoupling MPC for linear systems with bounded disturbances.

This paper presents a robust decoupling Model Predictive Controller (MPC) for constrained linear systems with bounded disturbances. The usual explicit decouplers are combined with a suitable augmented state–space representation in order to ensure recursive feasibility, robust constraint satisfaction, and cross-coupling reduction during set-point changes. The proposed approach is flexible with respect to the decoupler choice and the robust MPC algorithm. A robust MPC for tracking piecewise constant references with an artificial target based on nominal prediction is proposed in order to achieve offset-free piecewise constant reference tracking. Two case studies are presented to illustrate the effectiveness of the proposed decoupling strategy. • A robust decoupling MPC for tracking piecewise constant references has been proposed. • A new augmented model is used to provide a decoupled set-point tracking response. • Recursive feasibility and constraint satisfaction are ensured despite disturbances. [ABSTRACT FROM AUTHOR]