期刊
AUTOMATICA
卷 119, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.automatica.2020.109066
关键词
Infinite-dimensional systems; Modeling and control optimization; Controller constraints and structure; Model predictive control; Regular linear systems; Cayley-Tustin transform
资金
- Doctoral Program of Engineering and Natural Sciences of Tampere University of Technology (TUT), Finland
- International HR services of TUT, Finland
- Academy of Finland [310489]
- Academy of Finland (AKA) [310489, 310489] Funding Source: Academy of Finland (AKA)
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. (C) 2020 Elsevier Ltd. All rights reserved.
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