Heterogeneously parameterized tube model predictive control for LPV systems

Jurre Hanema (Corresponding author), Mircea Lazar, Roland Tóth

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

This paper presents a heterogeneously parameterized tube-based model predictive control (MPC) design applicable to linear parameter-varying (LPV) systems. In a heterogeneous tube, the parameterizations of the tube cross sections and the associated control laws are allowed to vary along the prediction horizon. Two extreme cases that can be described in this framework are scenario MPC (high complexity, larger domain of attraction) and homothetic tube MPC with a simple time-invariant control parameterization (low complexity, smaller domain of attraction). In the proposed framework, these extreme parameterizations, as well as other parameterizations of intermediate complexity, can be combined within a single tube. By allowing for more flexibility in the parameterization design, one can influence the trade-off between computational cost and the size of the domain of attraction. Sufficient conditions on the parameterization structure are developed under which recursive feasibility and closed-loop stability are guaranteed. A specific parameterization that combines the principles of scenario and homothetic tube MPC is proposed and it is shown to satisfy the required conditions. The properties of the approach, including its capability of achieving improved complexity/performance trade-offs, are demonstrated using two numerical examples.

Original languageEnglish
Article number108622
Number of pages13
JournalAutomatica
Volume111
DOIs
Publication statusPublished - 1 Jan 2020

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Model predictive control
Parameterization
Costs

Keywords

  • Constrained control
  • Linear parameter-varying systems
  • Robust model predictive control

Cite this

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Heterogeneously parameterized tube model predictive control for LPV systems. / Hanema, Jurre (Corresponding author); Lazar, Mircea; Tóth, Roland.

In: Automatica, Vol. 111, 108622, 01.01.2020.

Research output: Contribution to journalArticleAcademicpeer-review

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