Project number:
Title of the project:
Universal Explicit Model Predictive Controllers
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Other projects
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Date: 2011-2012

Abstract: The project aims at developing novel techniques for designing well-performing controllers which could be implemented on a cheap hardware. Using Model Predictive Control (MPC) and parametric programming, one can synthesize so-called explicit MPC controllers in form of a look-up table. Although such controllers provide very fast implentation, they suffer from the fact that if the prediction model changes, the whole look-up table needs to be re-computed, which is time-consuming. Therefore in this project we aim at synthesizing so-called universal explicit MPC controllers which can be adapted to changing parameters of the prediction model on-the-fly. This task will be achieved by transforming the prediction model into the Brunovsky canonical form, in which the state update is a bilinear function of the states and the time-varying model parameters. The universal controllers will subsequently be obtained by either replacing the bilinear term by auxiliary variables, or by approximating the bilinearity by a piecewise affine function.



Main scientific objective of the project is to develop novel theoretical methods leading to synthesis of explicit MPC controllers which can be adapted, on-the-fly, to time-varying changes of the model parameters.


Achieving the main goal involves solving three particular tasks:

  1. Transformation of the prediction model into the Brunovsky's canonical form.
  2. Synthesis of explicit MPC controllers for prediction models in the Brunovsky's form.
  3. Reduction of complexity of explicit MPC controllers such that they could be implemented on control platforms with severe limitations on available memory and CPU speed.


Principal researcher: Michal Kvasnica




  1. A. Szűcs – M. KvasnicaM. Fikar: A Memory-Efficient Representation of Explicit MPC Solutions. In Proceedings of the 50th CDC and ECC, Orlando, Florida, pp. 1916–1921, 2011.


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