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Model predictive control for regular linear systems

Research output: Contribution to journalArticleScientificpeer-review

Details

Original languageEnglish
Article number109066
JournalAutomatica
Volume119
DOIs
Publication statusPublished - 1 Sep 2020
Publication typeA1 Journal article-refereed

Abstract

The present work extends known finite-dimensional constrained optimal control realizations to the realm of well-posed regular linear infinite-dimensional systems modeled by partial differential equations. The structure-preserving Cayley–Tustin transformation is utilized to approximate the continuous-time system by a discrete-time model representation without using any spatial discretization or model reduction. The discrete-time model is utilized in the design of model predictive controller accounting for optimality, stabilization, and input and output/state constraints in an explicit way. The proposed model predictive controller is dual-mode in the sense that predictive controller steers the state to a set where exponentially stabilizing unconstrained feedback can be utilized without violating the constraints. The construction of the model predictive controller leads to a finite-dimensional constrained quadratic optimization problem easily solvable by standard numerical methods. Two representative examples of partial differential equations are considered.

Keywords

  • Cayley–Tustin transform, Controller constraints and structure, Infinite-dimensional systems, Model predictive control, Modeling and control optimization, Regular linear systems

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