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130626 ||| eng |
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|a 9781848002333
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| 100 |
1 |
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|a Huang, Biao
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| 245 |
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|a Dynamic Modeling, Predictive Control and Performance Monitoring
|h Elektronische Ressource
|b A Data-driven Subspace Approach
|c by Biao Huang, Ramesh Kadali
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| 250 |
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|a 1st ed. 2008
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| 260 |
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|a London
|b Springer London
|c 2008, 2008
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| 300 |
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|a XXIV, 242 p. 63 illus
|b online resource
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| 505 |
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|a 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
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| 653 |
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|a Mechanics, Applied
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| 653 |
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|a Control, Robotics, Automation
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| 653 |
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|a Applied Dynamical Systems
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| 653 |
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|a Control and Systems Theory
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| 653 |
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|a Control theory
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| 653 |
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|a Systems Theory, Control
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| 653 |
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|a Multibody Systems and Mechanical Vibrations
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| 653 |
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|a System theory
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| 653 |
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|a Nonlinear theories
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| 653 |
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|a Vibration
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| 653 |
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|a Chemistry, Technical
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| 653 |
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|a Control engineering
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| 653 |
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|a Robotics
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| 653 |
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|a Multibody systems
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| 653 |
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|a Automation
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| 653 |
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|a Industrial Chemistry
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| 653 |
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|a Dynamics
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| 700 |
1 |
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|a Kadali, Ramesh
|e [author]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
0 |
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|a Lecture Notes in Control and Information Sciences
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| 028 |
5 |
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|a 10.1007/978-1-84800-233-3
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| 856 |
4 |
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|u https://doi.org/10.1007/978-1-84800-233-3?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 629.8312
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| 082 |
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|a 003
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| 520 |
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|a 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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