Journal ArticleDOI
Paper: Model predictive heuristic control
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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.About:
This article is published in Automatica.The article was published on 1978-09-01. It has received 1835 citations till now. The article focuses on the topics: Adaptive control & Automatic control.read more
Citations
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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.
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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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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.
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Generalized predictive control—Part I. The basic algorithm
TL;DR: A novel method—generalized predictive control or GPC—is developed which is shown by simulation studies to be superior to accepted techniques such as generalized minimum-variance and pole-placement and to be a contender for general self-tuning applications.
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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.
References
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Journal ArticleDOI
System identification-A survey
Karl Johan Åström,Pieter Eykhoff +1 more
TL;DR: The survey explains the least squares method and several of its variants which may solve the problem of correlated residuals, viz. repeated and generalized least squares, maximum likelihood method, instrumental variable method, tally principle.
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A learning method for system identification
J. Nagumo,A. Noda +1 more
TL;DR: In this paper, a method for system identification is proposed which is based on the error-correcting training procedure in learning machines, and is referred to as learning identification, which is applicable to cases where the input signal is random and nonstationary, and can be completed within a short time, so that it may be used to identify linear quasi-time-invariant systems in which some parameters vary slowly in comparison with the time required for identification.
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Identification of processes in closed loop-identifiability and accuracy aspects
TL;DR: It is shown that prediction error identification methods, applied in a direct fashion will given correct estimates in a number of feedback cases, and the accuracy is not necessarily worse in the presence of feedback.