Topic
Model predictive control
About: Model predictive control is a research topic. Over the lifetime, 39662 publications have been published within this topic receiving 660470 citations.
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12 May 2009
TL;DR: In this article, the authors present methods for design and implementation of MPC systems using basis functions that confer the following advantages: continuous-and discrete-time MPC problems solved in similar design frameworks; a parsimonious parametric representation of the control trajectory gives rise to computationally efficient algorithms and better on-line performance; and a more general discrete time MPC design that becomes identical to the traditional approach for an appropriate choice of parameters.
Abstract: Model Predictive Control (MPC) is unusual in receiving on-going interest in both industrial and academic circles. Issues such as plant optimization and constrained control which are critical to industrial engineers are naturally embedded in its designs. Model Predictive Control System Design and Implementation Using MATLAB proposes methods for design and implementation of MPC systems using basis functions that confer the following advantages: continuous- and discrete-time MPC problems solved in similar design frameworks; a parsimonious parametric representation of the control trajectory gives rise to computationally efficient algorithms and better on-line performance; and a more general discrete-time representation of MPC design that becomes identical to the traditional approach for an appropriate choice of parameters. After the theoretical presentation, detailed coverage is given to three industrial applications: a food extruder, a motor and a magnetic bearing system. The subject of quadratic programming, often associated with the core optimization algorithms of MPC is also introduced and explained. The technical contents of this book, mainly based on advances in MPC using state-space models and basis functions to which the author is a major contributor, will be of interest to control researchers and practitioners, especially of process control. From a pedagogical standpoint, this volume includes numerous simple analytical examples and every chapter contains problems and MATLAB programs and exercises to assist the student.
1,310 citations
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TL;DR: It is proved that feasibility of the open-loop optimal control problem at time t = 0 implies asymptotic stability of the closed-loop system.
1,300 citations
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TL;DR: The paper revisits the operating principle of MPC and identifies three key elements in the MPC strategies, namely the prediction model, the cost function, and the optimization algorithm.
Abstract: Model predictive control (MPC) is a very attractive solution for controlling power electronic converters. The aim of this paper is to present and discuss the latest developments in MPC for power converters and drives, describing the current state of this control strategy and analyzing the new trends and challenges it presents when applied to power electronic systems. The paper revisits the operating principle of MPC and identifies three key elements in the MPC strategies, namely the prediction model, the cost function, and the optimization algorithm. This paper summarizes the most recent research concerning these elements, providing details about the different solutions proposed by the academic and industrial communities.
1,283 citations
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TL;DR: The relationship between GPC and LQ designs is investigated to show the computational advantage of the new approach and the robustness of the GPC approach to model over- and under-parameterization and to fast sampling rates is demonstrated by a set of simulations.
1,273 citations
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TL;DR: A classification of a number of decentralized, distributed and hierarchical control architectures for large scale systems is proposed and attention is focused on the design approaches based on Model Predictive Control.
1,234 citations