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: Model Predictive Control
    sub title: Approaches Based on the Extended State Space Model and Extended Non-minimal State Space Model
  name:
    name part: Zhang, Ridong.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Xue, Anke.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Gao, Furong.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: si
    date issued: 2019
    edition: 1st ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XV, 137 p. 28 illus., 25 illus. in color. online resource.
  abstract: This monograph introduces the authors' work on model predictive control system design using extended state space and extended non-minimal state space approaches. It systematically describes model predictive control design for chemical processes, including the basic control algorithms, the extension to predictive functional control, constrained control, closed-loop system analysis, model predictive control optimization-based PID control, genetic algorithm optimization-based model predictive control, and industrial applications. Providing important insights, useful methods and practical algorithms that can be used in chemical process control and optimization, it offers a valuable resource for researchers, scientists and engineers in the field of process system engineering and control engineering. .
  table of contents: Introduction -- Model Predictive Control Based on Extended State Space Model -- Predictive Functional Control Based on Extended State Space Model -- Model Predictive Control Based on Extended Non-Minimal State Space Model -- Predictive Functional Control Based on Extended Non-minimal State Space Model -- Model Predictive Control Under Constraints -- PID Control Using Extended Non-minimal State Space Model Optimization -- Closed-loop System Performance Analysis -- Model Predictive Control Performance Optimized by Genetic Algorithm -- Industrial Application -- Further Ideas on MPC and PFC Using Relaxed Constrained Optimization.
  note: by Ridong Zhang, Anke Xue, Furong Gao.
  subject:
    topic: Control engineering
  subject:
    topic: System theory
  subject:
    topic: Calculus of variations
  subject:
    topic: Energy efficiency
  subject:
    topic: Control and Systems Theory
  subject:
    topic: Systems Theory, Control
  subject:
    topic: Calculus of Variations and Optimal Control; Optimization
  subject:
    topic: Energy Efficiency
  classification: TJ212-225
  classification: 629.8
  related item:
    title info:
      title: Springer eBooks
  identifier: 9789811300837
  identifier: https://doi.org/10.1007/978-981-13-0083-7
  record info:
    record creation date: 180814
    record change date: 20191220130829.0
    record identifier: 978-981-13-0083-7