Most physical systems possess parametric uncertainties or unmeasurable parameters and, since parametric uncertainty may degrade the performance of model predictive control (MPC), mechanisms to update the unknown or uncertain parameters are desirable in application. One possibility is to apply adaptive extensions of MPC in which parameter estimation and control are performed online. This book proposes such an approach, with a design methodology for adaptive robust nonlinear MPC (NMPC) systems in the presence of disturbances and parametric uncertainties. One of the key concepts pursued is the concept of set-based adaptive parameter estimation, which provides a mechanism to estimate the unknown parameters as well as an estimate of the parameter uncertainty set. The knowledge of non-conservative uncertain set estimates is exploited in the design of robust adaptive NMPC algorithms that guarantee robustness of the NMPC system to parameter uncertainty.

• Chapter 1: Introduction
• Chapter 2: Optimal control
• Chapter 3: Review of nonlinear MPC
• Chapter 4: A real-time nonlinear MPC technique
• Chapter 5: Extensions for performance improvement
• Chapter 6: Introduction to adaptive robust MPC
• Chapter 7: Computational aspects of robust adaptive MPC
• Chapter 8: Finite-time parameter estimation in adaptive control
• Chapter 9: Performance improvement in adaptive control
• Chapter 10: Adaptive MPC for constrained nonlinear systems
• Chapter 11: Adaptive MPC with disturbance attenuation
• Chapter 12: Robust adaptive economic MPC
• Chapter 13: Set-based estimation in discrete-time systems
• Chapter 14: Robust adaptive MPC for discrete-time systems