Robust MPC formulations for perturbed constrained nonlinear systems

This work presents advanced model predictive control methods for nonlinear systems, with a focus on robustness, changing setpoints, and data-driven modeling. After introducing the main theoretical foundations of nonlinear MPC, it develops new control schemes for perturbed systems, including a robust tracking MPC and a contraction-based MPC for regulation when standard terminal ingredients are difficult to compute. The book also explores Gaussian Process-based MPC and concludes with a biomedical application to blood glucose regulation in subjects with type 1 diabetes.