Robust Optimal Control with Adjustable Uncertainty Sets

X. Zhang, M. Kamgarpour, A. Georghiou, P. J. Goulart and J. Lygeros

Automatica, vol. 75, no. 1, pp. 249-259, January 2017.
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  author = {X. Zhang and M. Kamgarpour and A. Georghiou and P. J. Goulart and J. Lygeros},
  title = {Robust Optimal Control with Adjustable Uncertainty Sets},
  journal = {Automatica},
  year = {2017},
  volume = {75},
  number = {1},
  pages = {249-259},
  url = {},
  doi = {10.1016/j.automatica.2016.09.016}

In this paper, we develop a unified framework for studying constrained robust optimal control problems with adjustable uncertainty sets. In contrast to standard constrained robust optimal control problems with known uncertainty sets, we treat the uncertainty sets in our problems as additional decision variables. In particular, given a finite prediction horizon and a metric for adjusting the uncertainty sets, we address the question of determining the optimal size and shape of the uncertainty sets, while simultaneously ensuring the existence of a control policy that will keep the system within its constraints for all possible disturbance realizations inside the adjusted uncertainty set. Since our problem subsumes the classical constrained robust optimal control design problem, it is computationally intractable in general. Nevertheless, we demonstrate that by restricting the families of admissible uncertainty sets and control policies, the problem can be formulated as a tractable convex optimization problem. We show that our framework captures several families of (convex) uncertainty sets of practical interest, and illustrate our approach on a demand response problem of providing control reserves for a power system.