Proceedings ArticleDOI
Scenario-based model predictive control of stochastic constrained linear systems
Daniele Bernardini,Alberto Bemporad +1 more
- pp 6333-6338
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TLDR
A stochastic model predictive control (MPC) formulation based on scenario generation for linear systems affected by discrete multiplicative disturbances is proposed, aimed at obtaining a less conservative control action with respect to classical robust MPC schemes, still enforcing convergence and feasibility properties for the controlled system.Abstract:
In this paper we propose a stochastic model predictive control (MPC) formulation based on scenario generation for linear systems affected by discrete multiplicative disturbances. By separating the problems of (1) stochastic performance, and (2) stochastic stabilization and robust constraints fulfillment of the closed-loop system, we aim at obtaining a less conservative control action with respect to classical robust MPC schemes, still enforcing convergence and feasibility properties for the controlled system. Stochastic performance is addressed for very general classes of stochastic disturbance processes, although discretized in the probability space, by adopting ideas from multi-stage stochastic optimization. Stochastic stability and recursive feasibility are enforced through linear matrix inequality (LMI) problems, which are solved off-line; stochastic performance is optimized by an on-line MPC problem which is formulated as a convex quadratically constrained quadratic program (QCQP) and solved in a receding horizon fashion. The performance achieved by the proposed approach is shown in simulation and compared to the one obtained by standard robust and deterministic MPC schemes.read more
Citations
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Book
Model Predictive Control
TL;DR: This paper recalls a few past achievements in Model Predictive Control, gives an overview of some current developments and suggests a few avenues for future research.
Journal ArticleDOI
Stochastic Model Predictive Control: An Overview and Perspectives for Future Research
TL;DR: In this article, a model predictive control (MPC) approach is proposed to solve an open-loop constrained optimal control problem (OCP) repeatedly in a receding-horizon manner, where the OCP is solved over a finite sequence of control actions at every sampling time instant that the current state of the system is measured.
Journal ArticleDOI
Multi-stage nonlinear model predictive control applied to a semi-batch polymerization reactor under uncertainty
TL;DR: In this paper, the authors present a robust non-conservative nonlinear model predictive control (MPC) approach based on the representation of the evolution of the uncertainty by a scenario tree, and leads to a non-ervative robust control of the uncertain plant because the adaptation of future inputs to new information is taken into account.
Journal ArticleDOI
All you need to know about model predictive control for buildings
Ján Drgoňa,Ján Drgoňa,Javier Arroyo,Iago Cupeiro Figueroa,David Blum,Krzysztof Arendt,Donghun Kim,Donghun Kim,Enric Perarnau Ollé,Juraj Oravec,Michael Wetter,Draguna Vrabie,Lieve Helsen +12 more
TL;DR: This paper provides a unified framework for model predictive building control technology with focus on the real-world applications and presents the essential components of a practical implementation of MPC such as different control architectures and nuances of communication infrastructures within supervisory control and data acquisition (SCADA) systems.
Journal ArticleDOI
Robust Model Predictive Control via Scenario Optimization
TL;DR: The proposed method may be a valid alternative when other existing techniques, either deterministic or stochastic, are not directly usable due to excessive conservatism or to numerical intractability caused by lack of convexity of the robust or chance-constrained optimization problem.
References
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Journal ArticleDOI
A survey of industrial model predictive control technology
S. Joe Qin,Thomas A. Badgwell +1 more
TL;DR: An overview of commercially available model predictive control (MPC) technology, both linear and nonlinear, based primarily on data provided by MPC vendors, is provided in this article, where a brief history of industrial MPC technology is presented first, followed by results of our vendor survey of MPC control and identification technology.
Book
Model Predictive Control
TL;DR: In this article, the authors present a model predictive controller for a water heating system, which is based on the T Polynomial Process (TOP) model of the MPC.
Journal ArticleDOI
Robust constrained model predictive control using linear matrix inequalities
TL;DR: This paper presents a new approach for robust MPC synthesis that allows explicit incorporation of the description of plant uncertainty in the problem formulation, and shows that the feasible receding horizon state-feedback control design robustly stabilizes the set of uncertain plants.
Book ChapterDOI
Robust model predictive control: A survey
Alberto Bemporad,Manfred Morari +1 more
TL;DR: The basic concepts of MPC are reviewed, the uncertainty descriptions considered in the MPC literature are surveyed, and the techniques proposed for robust constraint handling, stability, and performance are surveyed.
Journal ArticleDOI
Min-max feedback model predictive control for constrained linear systems
TL;DR: The control schemes the authors discuss introduce the notion that feedback is present in the receding-horizon implementation of the control, which leads to improved performance, compared to standard model predictive control, and resolves the feasibility difficulties that arise with the min-max techniques.