Offset-Free Output Feedback Tracking MPC for δISS Nonlinear Systems Subject to Input and Input Rate Constraints

This paper proposes an offset-free, output feedback, tracking Model Predictive Control (MPC) stabilizing formulation, specifically designed to handle incrementally Input-to-State Stable (δISS) systems subject to input and input rate constraints. Recursive feasibility and stability are guaranteed by means of suitable terminal ingredients, and an extended region of attraction is provided by means of artificial reference variables. Moreover, the knowledge of a suitable cost detectability function enables the use of a positive semidefinite stage cost (e.g. for output weighting), which can greatly simplify the controller tuning in case of high dimensional and/or black-box systems. Furthermore, offset-free tracking of asymptotically constant reference signals can be achieved even in presence of asymptotically constant disturbances, by means of a state observer that estimates the system state and output disturbances. The proposed MPC formulation is applied to control both linear and nonlinear systems, with two case studies inspired by remote pressure control in water systems, and pH neutralization processes. In particular, the second case study also discusses how to combine the proposed formulation with Recurrent Equilibrium Network (REN) models.