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Journal ArticleDOI

The stability of constrained receding horizon control

James B. Rawlings, +1 more
- 01 Oct 1993 - 
- Vol. 38, Iss: 10, pp 1512-1516
TLDR
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.
Abstract
An infinite horizon controller that allows incorporation of input and state constraints in a receding horizon feedback strategy is developed. For both stable and unstable linear plants, feasibility of the contraints guarantees nominal closed-loop stability for all choices of the tuning parameters in the control law. The constraints' feasibility can be checked efficiently with a linear program. It is always possible to remove state constraints in the early portion of the infinite horizon to make them feasible. The controller's implementation requires only the solution of finite-dimensional quadratic programs. >

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Citations
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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.
Journal ArticleDOI

A survey of industrial model predictive control technology

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.
Journal ArticleDOI

The explicit linear quadratic regulator for constrained systems

TL;DR: A technique to compute the explicit state-feedback solution to both the finite and infinite horizon linear quadratic optimal control problem subject to state and input constraints is presented, and it is shown that this closed form solution is piecewise linear and continuous.
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.
Journal ArticleDOI

Model predictive control: past, present and future

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.
References
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Book

Linear Optimal Control Systems

TL;DR: In this article, the authors provide an excellent introduction to feedback control system design, including a theoretical approach that captures the essential issues and can be applied to a wide range of practical problems.
Book

Mathematical Control Theory: Deterministic Finite Dimensional Systems

TL;DR: This book covers what constitutes the common core of control theory and is unique in its emphasis on foundational aspects, covering a wide range of topics written in a standard theorem/proof style and develops the necessary techniques from scratch.
Journal ArticleDOI

Paper: Model predictive heuristic control

TL;DR: In this paper, a new method of digital process control is described, which relies on three principles: 1) the multivariable plant is represented by its impulse responses which will be used on line by the control computer for long range prediction; 2) the behavior of the closed-loop system is prescribed by means of reference trajectories initiated on the actual outputs; 3) the control variables are computed in a heuristic way with the same procedure used in identification, which appears as a dual of the control under this formulation.
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