The Experts below are selected from a list of 4326363 Experts worldwide ranked by ideXlab platform
Francis J. Doyle - One of the best experts on this subject based on the ideXlab platform.
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Nonlinear Model-Based Control of a batch reactive distillation column
Journal of Process Control, 2000Co-Authors: Lalitha S. Balasubramhanya, Francis J. DoyleAbstract:Abstract The inherent trade off between model accuracy and computational tractability for Model-Based Control applications is addressed in this article by the development of reduced order nonlinear models. Traveling wave phenomena is used to develop low order models for multicomponent reactive distillation columns. A motivational example of batch esterification column is used to demonstrate the synthesis procedure. Tight Control of the column is obtained with the use of reduced model in a model predictive Control algorithm.
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Model based Control of a four-tank system
Computers & Chemical Engineering, 2000Co-Authors: Edward P. Gatzke, Edward S. Meadows, Chung Wang, Francis J. DoyleAbstract:Abstract A multi-disciplinary laboratory for Control education has been developed at the University of Delaware to expose students to realistic process system applications and advanced Control methods. One of the experiments is level Control of a four-tank system. This paper describes two Model-Based methods students can implement for Control of this interacting four-tank system. Sub-space identification is used to develop an empirical state space model of the experimental apparatus. This model is then used for model based Control using internal model Control (IMC). This represents an application of inner—outer factorization for non-minimum phase multivariable IMC design. Modeling is also performed using step tests and Aspen software for use with dynamic matrix Control (DMC).
Raymond Gorez - One of the best experts on this subject based on the ideXlab platform.
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Fuzzy and Quantitative Model-Based Control-systems for Robotic Manipulators
International Journal of Systems Science, 1993Co-Authors: M. De Neyer, Raymond GorezAbstract:Generally fuzzy Control systems use simple Controllers with a few inputs and one output. Here more complex Control systems, based explicitly on a model of the Controlled process and primarily developed in the frame of quantitative Control, are adapted to fuzzy Control. Three Model-Based Control schemes are proposed for position Control of a robotic manipulator. The feasibility of such Control systems and the ability of their quantitative and fuzzy implementations to cope with disturbances, parameter variations and unmodelled dynamics, are evaluated and compared by simulation analysis. The extension of the Model-Based Control paradigm to fuzzy Control pinpoints a concept unknown in the usual fuzzy Controllers, i.e. intrinsically fuzzy variables that may be a source of problems in fuzzy feedback loops.
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Fuzzy integral action in model based Control systems
[Proceedings 1993] Second IEEE International Conference on Fuzzy Systems, 1Co-Authors: M. De Neyer, Raymond Gorez, Jorge Muniz BarretoAbstract:Fuzzy-Model-Based Control and several schemes for its implementation are presented. The implementation of fuzzy integral actions introduced by means of these Control structures with a view to rejecting disturbances is discussed. The tracking and robustness capabilities of these Control systems are analyzed and evaluated by simulating the Control of a mechanical system. The simulation study shows that the performance of fuzzy-Model-Based Control systems in tracking and regulation is very good. >
Lalitha S. Balasubramhanya - One of the best experts on this subject based on the ideXlab platform.
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Nonlinear Model-Based Control of a batch reactive distillation column
Journal of Process Control, 2000Co-Authors: Lalitha S. Balasubramhanya, Francis J. DoyleAbstract:Abstract The inherent trade off between model accuracy and computational tractability for Model-Based Control applications is addressed in this article by the development of reduced order nonlinear models. Traveling wave phenomena is used to develop low order models for multicomponent reactive distillation columns. A motivational example of batch esterification column is used to demonstrate the synthesis procedure. Tight Control of the column is obtained with the use of reduced model in a model predictive Control algorithm.
M. De Neyer - One of the best experts on this subject based on the ideXlab platform.
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Fuzzy and Quantitative Model-Based Control-systems for Robotic Manipulators
International Journal of Systems Science, 1993Co-Authors: M. De Neyer, Raymond GorezAbstract:Generally fuzzy Control systems use simple Controllers with a few inputs and one output. Here more complex Control systems, based explicitly on a model of the Controlled process and primarily developed in the frame of quantitative Control, are adapted to fuzzy Control. Three Model-Based Control schemes are proposed for position Control of a robotic manipulator. The feasibility of such Control systems and the ability of their quantitative and fuzzy implementations to cope with disturbances, parameter variations and unmodelled dynamics, are evaluated and compared by simulation analysis. The extension of the Model-Based Control paradigm to fuzzy Control pinpoints a concept unknown in the usual fuzzy Controllers, i.e. intrinsically fuzzy variables that may be a source of problems in fuzzy feedback loops.
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Fuzzy integral action in model based Control systems
[Proceedings 1993] Second IEEE International Conference on Fuzzy Systems, 1Co-Authors: M. De Neyer, Raymond Gorez, Jorge Muniz BarretoAbstract:Fuzzy-Model-Based Control and several schemes for its implementation are presented. The implementation of fuzzy integral actions introduced by means of these Control structures with a view to rejecting disturbances is discussed. The tracking and robustness capabilities of these Control systems are analyzed and evaluated by simulating the Control of a mechanical system. The simulation study shows that the performance of fuzzy-Model-Based Control systems in tracking and regulation is very good. >
Edward P. Gatzke - One of the best experts on this subject based on the ideXlab platform.
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Model based Control of a four-tank system
Computers & Chemical Engineering, 2000Co-Authors: Edward P. Gatzke, Edward S. Meadows, Chung Wang, Francis J. DoyleAbstract:Abstract A multi-disciplinary laboratory for Control education has been developed at the University of Delaware to expose students to realistic process system applications and advanced Control methods. One of the experiments is level Control of a four-tank system. This paper describes two Model-Based methods students can implement for Control of this interacting four-tank system. Sub-space identification is used to develop an empirical state space model of the experimental apparatus. This model is then used for model based Control using internal model Control (IMC). This represents an application of inner—outer factorization for non-minimum phase multivariable IMC design. Modeling is also performed using step tests and Aspen software for use with dynamic matrix Control (DMC).
