Process Modelling for Control [electronic resource] : A Unified Framework Using Standard Black-box Techniques / by Benoît Codrons.

By: Codrons, Benoît [author.]Contributor(s): SpringerLink (Online service)Material type: TextTextLanguage: English Series: Advances in Industrial Control: Publisher: London : Springer London, 2005Description: XXXIII, 229 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9781846282478Subject(s): Engineering | Chemical engineering | Computer simulation | Mechanical engineering | Engineering | Control Engineering | Simulation and Modeling | Industrial Chemistry/Chemical Engineering | Mechanical EngineeringAdditional physical formats: Printed edition:: No titleOnline resources: Click here to access online
Contents:
Preliminary Material -- Identification in Closed Loop for Better Control Design -- Dealing with Controller Singularities in Closed-loop Identification -- Model and Controller Validation for Robust Control in a Prediction-error Framework -- Control-oriented Model Reduction and Controller Reduction -- Some Final Words.
In: Springer eBooksSummary: Many process control books focus on control design techniques, taking the construction of a process model for granted. Process Modelling for Control concentrates on the modelling steps underlying a successful design, answering questions like: How should I carry out the identification of my process in order to obtain a good model? How can I assess the quality of a model with a view to using it in control design? How can I ensure that a controller will stabilise a real process and achieve a pre-specified level of performance before implementation? What is the most efficient method of order reduction to facilitate the implementation of high-order controllers? Different tools, namely system identification, model/controller validation and order reduction are studied in a framework with a common basis: closed-loop identification with a controller that is close to optimal will deliver models with bias and variance errors ideally tuned for control design. As a result, rules are derived, applying to all the methods, that provide the practitioner with a clear way forward despite the apparently unconnected nature of the modelling tools. Detailed worked examples, representative of various industrial applications, are given: control of a mechanically flexible structure; a chemical process; and a nuclear power plant. Process Modelling for Control uses mathematics of an intermediate level convenient to researchers with an interest in real applications and to practising control engineers interested in control theory. It will enable working control engineers to improve their methods and will provide academics and graduate students with an all-round view of recent results in modelling for control. Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.
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Preliminary Material -- Identification in Closed Loop for Better Control Design -- Dealing with Controller Singularities in Closed-loop Identification -- Model and Controller Validation for Robust Control in a Prediction-error Framework -- Control-oriented Model Reduction and Controller Reduction -- Some Final Words.

Many process control books focus on control design techniques, taking the construction of a process model for granted. Process Modelling for Control concentrates on the modelling steps underlying a successful design, answering questions like: How should I carry out the identification of my process in order to obtain a good model? How can I assess the quality of a model with a view to using it in control design? How can I ensure that a controller will stabilise a real process and achieve a pre-specified level of performance before implementation? What is the most efficient method of order reduction to facilitate the implementation of high-order controllers? Different tools, namely system identification, model/controller validation and order reduction are studied in a framework with a common basis: closed-loop identification with a controller that is close to optimal will deliver models with bias and variance errors ideally tuned for control design. As a result, rules are derived, applying to all the methods, that provide the practitioner with a clear way forward despite the apparently unconnected nature of the modelling tools. Detailed worked examples, representative of various industrial applications, are given: control of a mechanically flexible structure; a chemical process; and a nuclear power plant. Process Modelling for Control uses mathematics of an intermediate level convenient to researchers with an interest in real applications and to practising control engineers interested in control theory. It will enable working control engineers to improve their methods and will provide academics and graduate students with an all-round view of recent results in modelling for control. Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

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