Journal ArticleDOI
Internal model control. A unifying review and some new results
Carlos E. García,Manfred Morari +1 more
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This article is published in Industrial & Engineering Chemistry Process Design and Development.The article was published on 1982-04-01. It has received 1373 citations till now. The article focuses on the topics: Internal model.read more
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Model predictive control: theory and practice—a survey
TL;DR: The flexible constraint handling capabilities of MPC are shown to be a significant advantage in the context of the overall operating objectives of the process industries and the 1-, 2-, and ∞-norm formulations of the performance objective are discussed.
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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.
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Model predictive control: past, present and future
Manfred Morari,Jay H. Lee +1 more
TL;DR: In this article, a theoretical basis for model predictive control (MPC) has started to emerge and many practical problems like control objective prioritization and symptom-aided diagnosis can be integrated into the MPC framework by expanding the problem formulation to include integer variables yielding a mixed-integer quadratic or linear program.
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Neural networks for control systems: a survey
TL;DR: In this paper, the authors focus on the promise of artificial neural networks in the realm of modelling, identification and control of nonlinear systems and explore the links between the fields of control science and neural networks.
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The stability of constrained receding horizon control
TL;DR: An infinite horizon controller that allows incorporation of input and state constraints in a receding horizon feedback strategy is developed and guarantees nominal closed-loop stability for all choices of the tuning parameters in the control law.