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Dynamic Modeling, Predictive Control and Performance Monitoring : A Data-driven Subspace Approach /

By: Huang, Biao [author.].
Contributor(s): Kadali, Ramesh [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Control and Information Sciences: 374Publisher: London : Springer London, 2008.Description: XXIV, 242 p. 63 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9781848002333.Subject(s): Engineering | Chemical engineering | System theory | Complexity, Computational | Vibration | Dynamical systems | Dynamics | Control engineering | Robotics | Mechatronics | Engineering | Control | Systems Theory, Control | Industrial Chemistry/Chemical Engineering | Vibration, Dynamical Systems, Control | Control, Robotics, Mechatronics | ComplexityDDC classification: 629.8 Online resources: Click here to access online
Contents:
I Dynamic Modeling through Subspace Identification -- System Identification: Conventional Approach -- Open-loop Subspace Identification -- Closed-loop Subspace Identification -- Identification of Dynamic Matrix and Noise Model Using Closed-loop Data -- II Predictive Control -- Model Predictive Control: Conventional Approach -- Data-driven Subspace Approach to Predictive Control -- III Control Performance Monitoring -- Control Loop Performance Assessment: Conventional Approach -- State-of-the-art MPC Performance Monitoring -- Subspace Approach to MIMO Feedback Control Performance Assessment -- Prediction Error Approach to Feedback Control Performance Assessment -- Performance Assessment with LQG-benchmark from Closed-loop Data.
In: Springer eBooksSummary: A typical design procedure for model predictive control or control performance monitoring consists of: 1. identification of a parametric or nonparametric model; 2. derivation of the output predictor from the model; 3. design of the control law or calculation of performance indices according to the predictor. Both design problems need an explicit model form and both require this three-step design procedure. Can this design procedure be simplified? Can an explicit model be avoided? With these questions in mind, the authors eliminate the first and second step of the above design procedure, a “data-driven” approach in the sense that no traditional parametric models are used; hence, the intermediate subspace matrices, which are obtained from the process data and otherwise identified as a first step in the subspace identification methods, are used directly for the designs. Without using an explicit model, the design procedure is simplified and the modelling error caused by parameterization is eliminated.
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Item type Current location Call number Status Date due Barcode Item holds
E books E books PK Kelkar Library, IIT Kanpur
Available EBK665
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I Dynamic Modeling through Subspace Identification -- System Identification: Conventional Approach -- Open-loop Subspace Identification -- Closed-loop Subspace Identification -- Identification of Dynamic Matrix and Noise Model Using Closed-loop Data -- II Predictive Control -- Model Predictive Control: Conventional Approach -- Data-driven Subspace Approach to Predictive Control -- III Control Performance Monitoring -- Control Loop Performance Assessment: Conventional Approach -- State-of-the-art MPC Performance Monitoring -- Subspace Approach to MIMO Feedback Control Performance Assessment -- Prediction Error Approach to Feedback Control Performance Assessment -- Performance Assessment with LQG-benchmark from Closed-loop Data.

A typical design procedure for model predictive control or control performance monitoring consists of: 1. identification of a parametric or nonparametric model; 2. derivation of the output predictor from the model; 3. design of the control law or calculation of performance indices according to the predictor. Both design problems need an explicit model form and both require this three-step design procedure. Can this design procedure be simplified? Can an explicit model be avoided? With these questions in mind, the authors eliminate the first and second step of the above design procedure, a “data-driven” approach in the sense that no traditional parametric models are used; hence, the intermediate subspace matrices, which are obtained from the process data and otherwise identified as a first step in the subspace identification methods, are used directly for the designs. Without using an explicit model, the design procedure is simplified and the modelling error caused by parameterization is eliminated.

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