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: Discrete-Time High Order Neural Control
    sub title: Trained with Kaiman Filtering
  name:
    name part: Sanchez, Edgar N.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Alanís, Alma Y.
    role:
      role term: author.
  name:
    name part: Loukianov, Alexander G.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2008
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: X, 110 p. online resource.
  abstract: The objective of this work is to present recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple outputs. The results that appear in each chapter include rigorous mathematical analyses, based on the Lyapunov approach, in order to guarantee its properties; in addition, for each chapter, simulation results are included to verify the successful performance of the corresponding proposed schemes. In order to complete the treatment of these schemes, the book includes a chapter presenting experimental results related to their application to an electric three phase induction motor, which show the applicability of such designs. The proposed schemes could be employed for different applications beyond the ones presented in this book. The book presents solutions for the output trajectory tracking problem of unknown nonlinear systems based on four schemes. For the first one, a direct design method is considered: the well known backstepping method, under the assumption of complete state measurement; the second one considers an indirect method, solved with the block control and the sliding mode techniques, under the same assumption. For the third scheme, the backstepping technique is reconsidering including a neural observer, and finally the block control and the sliding mode techniques are used again too, with a neural observer. All the proposed schemes are developed in discrete-time. For both mentioned control methods as well as for the neural observer, the on-line training of the respective neural networks is performed by Kalman Filtering.
  table of contents: Mathematical Preliminaries -- Discrete-Time Adaptive Neural Backstepping -- Discrete-Time Block Control -- Discrete-Time Neural Observers -- Discrete-Time Output Trajectory Tracking -- Real Time Implementation -- Conclusions and Future Work.
  note: by Edgar N. Sanchez, Alma Y. Alanís, Alexander G. Loukianov.
  subject:
    topic: Engineering
  subject:
    topic: Artificial intelligence
  subject:
    topic: System theory
  subject:
    topic: Statistical physics
  subject:
    topic: Dynamical systems
  subject:
    topic: Applied mathematics
  subject:
    topic: Engineering mathematics
  subject:
    topic: Control engineering
  subject:
    topic: Robotics
  subject:
    topic: Mechatronics
  subject:
    topic: Engineering
  subject:
    topic: Appl.Mathematics/Computational Methods of Engineering
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  subject:
    topic: Statistical Physics, Dynamical Systems and Complexity
  subject:
    topic: Control, Robotics, Mechatronics
  subject:
    topic: Systems Theory, Control
  classification: TA329-348
  classification: TA640-643
  classification: 519
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Studies in Computational Intelligence, 112
  identifier: 9783540782896
  identifier: http://dx.doi.org/10.1007/978-3-540-78289-6
  record info:
    record creation date: 100301
    record change date: 20151204183532.0
    record identifier: 978-3-540-78289-6