Author
Thomas A. Badgwell
Other affiliations: SEMATECH, Rice University, University of Texas at Austin ...read more
Bio: Thomas A. Badgwell is an academic researcher from ExxonMobil. The author has contributed to research in topics: Model predictive control & Robust control. The author has an hindex of 17, co-authored 35 publications receiving 6437 citations. Previous affiliations of Thomas A. Badgwell include SEMATECH & Rice University.
Papers
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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.
4,819 citations
01 Jan 2000
TL;DR: In this article, the authors provide an overview of nonlinear model predictive control (NMPC) applications in industry, focusing primarily on recent applications reported by NMPC vendors, and present five industrial NMPC implementations with reference to modeling, control, optimization and implementation issues.
Abstract: This paper provides an overview of nonlinear model predictive control (NMPC) applications in industry, focusing primarily on recent applications reported by NMPC vendors. A brief summary of NMPC theory is presented to highlight issues pertinent to NMPC applications. Five industrial NMPC implementations are then discussed with reference to modeling, control, optimization, and implementation issues. Results from several industrial applications are presented to illustrate the benefits possible with NMPC technology. A discussion of future needs in NMPC theory and practice is provided to conclude the paper.
487 citations
01 Jan 1999
TL;DR: This work states that nonlinear model predictive control, i.e. MPC based on a nonlinear plant description, has only emerged in the past decade and the number of reported industrial applications is still fairly low.
Abstract: In the past decade model predictive control (MPC) has become a preferred control strategy for a large number of processes. The main reasons for this preference include the ability to handle constraints in an optimal way and the flexible formulation in the time domain. Linear MPC schemes, i.e. MPC schemes for which the prediction is based on a linear description of the plant, are by now routinely used in a number of industrial sectors and the underlying control theoretic problems, like stability, are well studied. Nonlinear model predictive control (NMPC), i.e. MPC based on a nonlinear plant description, has only emerged in the past decade and the number of reported industrial applications is still fairly low. Because of its additional ability to take process nonlinearities into account, expectations on this control methodology are high.
476 citations
TL;DR: In this article, a general disturbance model that accommodates unmeasured disturbances entering through the process input, state, or output is presented, and conditions for which offset-free control is possible are stated for the combined estimator, steady-state target calculation, and dynamic controller.
441 citations
TL;DR: In this paper, a method and apparatus for steady-state target calculation that explicitly accounts for model uncertainty is presented, and a nominal estimate of the system parameters G is made, and the steady state targets are selected such that when G = G, the system is driven to an operational steady state in which the objective function is extremized.
Abstract: A method and apparatus for steady-state target calculation that explicitly accounts for model uncertainty is disclosed. In accordance with one aspect of the invention, when model uncertainty is incorporated, the linear program associated with the steady-state target calculation can be recast as a highly structured nonlinear program. In accordance with another aspect of the invention, primal-dual interior point methods can be applied to take advantage of the resulting special structure. For a system having characteristic gain parameters G having a known uncertainty description, the present invention provides a method and apparatus for selecting steady-state targets for said system-manipulated variables such that all system-controlled variables will remain feasible at steady-state for all possible values of the parameters G within the known uncertainty description. A nominal estimate {tilde over (G)} of the system parameters G is made, and in accordance with another aspect of the invention, the steady-state targets are selected such that when {tilde over (G)}=G, the system is driven to an operational steady-state in which the objective function is extremized.
182 citations
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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.
8,064 citations
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.
4,819 citations
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.
3,187 citations
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.
2,320 citations
Book•
01 Dec 2004TL;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.
Abstract: 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.
1,897 citations