Control

Model predictive control is an advanced control method, based of models and optimization (optimal control). It has proven its performance on several industrial applications (more than 1000 applications identified in the early 2000s). At the same time, the theory has continued to evolve and many advances have been made, mainly on the improvement of robustness and real- time performance on the one hand, and on the extension to more complex structures / objectives on the other hand. The objective of this course is to present the classical or more recent results on model predictive control, which will allow the use of this technique as a methodological tool in automatic control. An important part of the course will be dedicated to the optimization and implementation aspect, where the taking into account of constraints leads to solve control problems by real time optimization techniques which are heavy in terms of computation even if the prediction models are linear. An alternative solution based on the search for explicit solutions for real-time implementation will be presented. In the second part, the course will deal with extensions to cover the notions of robustness of the prediction. Prediction in the presence of model uncertainties represents the key point of design but the mastery of computational complexity remains an unavoidable constraint. The course aims to raise awareness of the use of more complex models that take into account hybrid or non-linear dynamics in general.

References

  1. Qin, S. Joe, and Thomas A. Badgwell. “A survey of industrial model predictive control technology.” Control engineering practice 11.7 (2003): 733-764.
  2. Mayne, D. Q., Rawlings, J. B., Rao, C. V., & Scokaert, P. O. (2000). Constrained model predictive control: Stability and optimality. Automatica, 36(6), 789-814.
  3. Bemporad, A., Morari, M., Dua, V., & Pistikopoulos, E. N. (2002). The explicit linear quadratic regulator for constrained systems. Automatica, 38(1), 3-20.
  4. Mayne, D. Q., Seron, M. M., & Raković, S. V. (2005). Robust model predictive control of constrained linear systems with bounded disturbances. Automatica, 41(2), 219-224.
  5. Rawlings, J. B., & Mayne, D. Q. (2009). Model predictive control: Theory and design (pp. 3430-3433). Madison, Wisconsin: Nob Hill Pub.
  6. Fernandez-Camacho, E., & Bordons-Alba, C. (1995). Model predictive control in the process industry. Springer London.
  7. Boucher, P., & Dumur, D. (2006). La Commande Prédictive: Avancées et perspectives, Traité IC2.