Optimal Prioritized Infeasibility Handling in Model Predictive Control: Parametric Preemptive Multiobjective Linear Programming Approach

J. Vada, O. Slupphaug and T. A. Johansen
Additional contact information
J. Vada: Energos
O. Slupphaug: ABB Industry
T. A. Johansen: Norwegian University of Science and Technology

Journal of Optimization Theory and Applications, 2001, vol. 109, issue 2, No 8, 385-413

Abstract: Abstract All practical implementations of model-based predictive control (MPC) require a means to recover from infeasibility. We propose a strategy designed for linear state-space MPC with prioritized constraints. It relaxes optimally an infeasible MPC optimization problem into a feasible one by solving a single-objective linear program (LP) online in addition to the standard online MPC optimization problem at each sample. By optimal, it is meant that the violation of a lower prioritized constraint cannot be made less without increasing the violation of a higher prioritized constraint. The problem of computing optimal constraint violations is naturally formulated as a parametric preemptive multiobjective LP. By extending well-known results from parametric LP, the preemptive multiobjective LP is reformulated into an equivalent standard single-objective LP. An efficient algorithm for offline design of this LP is given, and the algorithm is illustrated on an example.

Keywords: parametric programming; preemptive programming; infeasibility; linear model predictive control (search for similar items in EconPapers)
Date: 2001
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Citations: View citations in EconPapers (1)

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DOI: 10.1023/A:1017570507125

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