Note: This content is accessible to all versions of every browser. However, this browser does not seem to support current Web standards, preventing the display of our site's design details.


Suboptimal explicit MPC via approximate multiparametric quadratic programming


A. Bemporad, C. Filippi

IEEE Conference on Decision and Control, Orlando, Florida

Algorithms for solving multiparametric quadratic programming (mp-QP) were proposed in Bemporad et al. (2001) and Tondel et al. (2001) for computing explicit model predictive control (MPC) laws. The reason for this interest is that the solution to mp-QP is a piecewise affine function of the state vector and thus it is easily implementable on-line. The main drawback of solving mp-QP exactly is that whenever the number of linear constraints involved in the optimization problem increases, the number of polyhedral cells in the piecewise affine partition of the parameter space may increase exponentially. We address the problem of finding approximate solutions to mp-QP, where the degree of approximation is arbitrary and allows a trade off between optimality and a smaller number of cells in the piecewise affine solution.


Type of Publication:


File Download:

Request a copy of this publication.
(Uses JavaScript)
% Autogenerated BibTeX entry
@InProceedings { BemFil:2001:IFA_1685,
    author={A. Bemporad and C. Filippi},
    title={{Suboptimal explicit MPC via approximate multiparametric
	  quadratic programming}},
    booktitle={IEEE Conference on Decision and Control},
    address={Orlando, Florida},
Permanent link