Book ChapterDOI
Input-to-State Stability: A Unifying Framework for Robust Model Predictive Control
Daniel Limon,Teodoro Alamo,Davide M. Raimondo,D. Muñoz de la Peña,José Manuel Bravo,Antonio Ferramosca,Eduardo F. Camacho +6 more
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TLDR
In this paper, the robustness of MPC for constrained uncertain nonlinear systems is investigated in the presence of constraints on the system and of the possible discontinuity of the control law.Abstract:
This paper deals with the robustness of Model Predictive Controllers for constrained uncertain nonlinear systems. The uncertainty is assumed to be modeled by a state and input dependent signal and a disturbance signal. The framework used for the analysis of the robust stability of the systems controlled by MPC is the wellknown Input-to-State Stability. It is shown how this notion is suitable in spite of the presence of constraints on the system and of the possible discontinuity of the control law.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
Provably safe and robust learning-based model predictive control
TL;DR: A learning-based model predictive control scheme that provides deterministic guarantees on robustness, while statistical identification tools are used to identify richer models of the system in order to improve performance.
Proceedings ArticleDOI
Stochastic nonlinear model predictive control with probabilistic constraints
TL;DR: The capability of the stochastic model predictive control approach in terms of shaping the probability distribution of system states and fulfilling state constraints in a Stochastic setting is demonstrated for optimal control of polymorphic transformation in batch crystallization.
Journal ArticleDOI
Lyapunov stability of economically oriented NMPC for cyclic processes
TL;DR: In this paper, the authors proposed two economically oriented nonlinear model predictive control (NMPC) formulations and proved nominal stability for both formulations, and showed that the asymptotic stability of the transformed system is equivalent to that of the original system.
Journal ArticleDOI
Predictive Control of Power Converters: Designs With Guaranteed Performance
TL;DR: A cost function design based on Lyapunov stability concepts for finite control set model predictive control allows one to characterize the performance of the controlled converter, while providing sufficient conditions for local stability for a class of power converters.
References
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Journal ArticleDOI
Survey Constrained model predictive control: Stability and optimality
TL;DR: This review focuses on model predictive control of constrained systems, both linear and nonlinear, and distill from an extensive literature essential principles that ensure stability to present a concise characterization of most of the model predictive controllers that have been proposed in the literature.
Book
Predictive Control With Constraints
TL;DR: A standard formulation of Predictive Control is presented, with examples of step response and transfer function formulations, and a case study of robust predictive control in the context of MATLAB.
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.