rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Dynamic Modeling, Predictive Control and Performance Monitoring
    sub title: A Data-driven Subspace Approach
  name:
    name part: Huang, Biao.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Kadali, Ramesh.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxk
    date issued: 2008
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XXIV, 242 p. 63 illus. online resource.
  abstract: 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.
  table of 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.
  note: by Biao Huang, Ramesh Kadali.
  subject:
    topic: Engineering
  subject:
    topic: Chemical engineering
  subject:
    topic: System theory
  subject:
    topic: Complexity, Computational
  subject:
    topic: Vibration
  subject:
    topic: Dynamical systems
  subject:
    topic: Dynamics
  subject:
    topic: Control engineering
  subject:
    topic: Robotics
  subject:
    topic: Mechatronics
  subject:
    topic: Engineering
  subject:
    topic: Control
  subject:
    topic: Systems Theory, Control
  subject:
    topic: Industrial Chemistry/Chemical Engineering
  subject:
    topic: Vibration, Dynamical Systems, Control
  subject:
    topic: Control, Robotics, Mechatronics
  subject:
    topic: Complexity
  classification: TJ212-225
  classification: 629.8
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Lecture Notes in Control and Information Sciences, 374
  identifier: 9781848002333
  identifier: http://dx.doi.org/10.1007/978-1-84800-233-3
  record info:
    record creation date: 100301
    record change date: 20151204170834.0
    record identifier: 978-1-84800-233-3