Model Predictive Control
Course objective
In modern engineering, making real-time decisions while accounting for constraints, uncertainties, and optimality is a key challenge. Model Predictive Control (MPC) provides a powerful framework to tackle this problem, enabling advanced control strategies in robotics, energy systems, autonomous vehicles, and industrial automation. This course blends theory with practical applications, emphasizing how optimization and robustness shape control performance. By exploring both fundamental principles and cutting-edge formulations, students will gain the skills to design predictive controllers that enhance efficiency, safety, and adaptability in complex engineering systems.
Course content
- General formulations and structural properties of MPC
- Design for stability and optimality (Linear MPC)
- The computational aspects and challanges (Linear MPC)
- Robust MPC: the principles and basic formulations
- Robust MPC: advanced formulations and their computational features
- Parameter varying, hybrid and other MPC structures
References
D. Q. Mayne, “Model Predictive Control: Recent Developments and Future Promise”, Automatica, Vol. 50, No. 12, 2014, pp. 2967–2986. J. M. Maciejowski, Predictive Control with Constraints, Pearson, 2002. J. B. Rawlings, D. Q. Mayne, and M. M. Diehl, Model Predictive Control: Theory, Computation, and Design, 2nd Edition, Nob Hill Publishing, 2020. E. F. Camacho and C. Bordons, Model Predictive Control, 2nd Edition, Springer, 2007. J. Köhler, C. Berberich, F. Allgöwer, and P. J. Antsaklis, “Learning-Based Model Predictive Control: Toward Safe Learning in Control”, Annual Review of Control, Robotics, and Autonomous Systems, Vol. 3, 2020, pp. 269–296.
