last modified February 12, 2014 - 14:59 CET

Model predictive control

Model predictive control (MPC) has become the accepted standard for complex constrained multivariable control problems in the process industries. Over the last decades, a solid theoretical foundation for MPC has emerged so that in real life, large-scale MIMO applications controllers with non-conservative stability guarantees can be designed routinely and with ease. The big drawback of the MPC is the relatively formidable on-line computational effort, which limits its applicability to relatively slow and/or small problems.

Our research efforts aim at minimizing the on-line computational effort by

  • developing a PWL control law based on a suboptimal solution to the MPC problem;
  • implementing the control action on digital circuits

Involved People:

Main Publications:

  • Bemporad A, Oliveri A, Poggi T and Storace M (2011), "Ultra-Fast Stabilizing Model Predictive Control via Canonical Piecewise Affine Approximations", Automatic Control, IEEE Transactions on. Vol. 56(12), pp. 2883 -2897.

Funding:

  • This research activity was partially supported by the European Community through the MOBY-DIC project (FP7-IST-248858)

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