Journal ArticleDOI
Stochastic model predictive control with active uncertainty learning: A Survey on dual control
TLDR
The stochastic MPC (SMPC) problem in the dual control paradigm is presented, where the control inputs to an uncertain system have a probing effect for active uncertainty learning and a directing effect for controlling the system dynamics.About:
This article is published in Annual Reviews in Control.The article was published on 2017-11-20. It has received 141 citations till now. The article focuses on the topics: Dual control theory & Stochastic control.read more
Citations
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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
Stochastic model predictive control — how does it work?
TL;DR: An overview of core concepts in SMPC in relation to MPC and stochastic optimal control is presented, with numerical illustrations on a typical chemical process.
Journal ArticleDOI
An efficient method for stochastic optimal control with joint chance constraints for nonlinear systems
Joel A. Paulson,Ali Mesbah +1 more
TL;DR: The proposed solution method is significantly superior to a standard random sampling method for stochastic optimal control in terms of computational requirements and the moment-based surrogate for the JCC is shown to be substantially less conservative than the widely used distributionally robust Cantelli-Chebyshev inequality for chance constraint approximation.
Journal ArticleDOI
Near-optimal control of nonlinear dynamical systems: A brief survey
Yinyan Zhang,Shuai Li,Liefa Liao +2 more
TL;DR: This paper serves as a brief survey for existing methods in this research direction and reports on methods reported to approximately solve the problem leading to the so-called near-optimal control.
Journal ArticleDOI
Fast approximate learning-based multistage nonlinear model predictive control using Gaussian processes and deep neural networks
TL;DR: A learning-based multistage MPC for systems with hard-to-model dynamics and time-varying plant-model mismatch is presented, and the approximate LB-msMPC strategy is demonstrated on a cold atmospheric plasma jet with applications in (bio)materials processing.
References
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System Identification: Theory for the User
TL;DR: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis and praktische Anwendung der verschiedenen Verfahren zur IdentifIZierung hat.
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Dynamic Programming
TL;DR: The more the authors study the information processing aspects of the mind, the more perplexed and impressed they become, and it will be a very long time before they understand these processes sufficiently to reproduce them.
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Dynamic Programming and Optimal Control
TL;DR: The leading and most up-to-date textbook on the far-ranging algorithmic methododogy of Dynamic Programming, which can be used for optimal control, Markovian decision problems, planning and sequential decision making under uncertainty, and discrete/combinatorial optimization.
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Adaptive Control
TL;DR: Benefiting from the feedback of users who are familiar with the first edition, the material has been reorganized and rewritten, giving a more balanced and teachable presentation of fundamentals and applications.