Last edited by Zologor
Tuesday, May 19, 2020 | History

8 edition of Neural Network Control of Nonlinear Discrete-Time Systems (Public Administration and Public Policy) found in the catalog.

Neural Network Control of Nonlinear Discrete-Time Systems (Public Administration and Public Policy)

by Jagannathan Sarangapani

  • 9 Want to read
  • 19 Currently reading

Published by CRC .
Written in English

    Subjects:
  • Automatic control engineering,
  • Nonlinear control theory,
  • Linear Programming,
  • Technology,
  • Mathematics,
  • Science/Mathematics,
  • Technology / Electronics / General,
  • Intelligent control systems,
  • Electronics - General,
  • Engineering - General,
  • Neural networks (Computer science)

  • The Physical Object
    FormatHardcover
    Number of Pages602
    ID Numbers
    Open LibraryOL8125399M
    ISBN 100824726774
    ISBN 109780824726775

    The book also proposes the use of recurrent neural networks to model discrete-time nonlinear systems. Combined with the inverse optimal control approach, such models constitute a powerful tool to deal with uncertainties such as unmodeled dynamics and disturbances. In Chapter 7, we discuss the system identification by developing suitable nonlinear identifier models for a broad class of nonlinear discrete-time systems using neural networks. In Chapter 8, model reference adaptive control of a class of nonlinear discrete-time systems is treated.

    The book presents recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple outputs. The simulation results that appear in each chapter include rigorous mathematical analyses, based on the Lyapunov approach, to establish its properties. There has been great interest in "universal controllers" that mimic the functions of human processes to learn about the systems they are controlling on-line so that performance improves automatically. Neural network controllers are derived for robot manipulators in a variety of applications Price: $

      The book presents recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple outputs. The simulation results that appear in each chapter include rigorous mathematical analyses, based on Author: Edgar Sanchez. Neural Network Control Systems. Control nonlinear systems using model-predictive, NARMA-L2, and model-reference neural networks Learn how the Neural Network Predictive Controller uses a neural network model of a nonlinear plant to predict future plant performance. Design NARMA-L2 Neural Controller in Simulink.


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Neural Network Control of Nonlinear Discrete-Time Systems (Public Administration and Public Policy) by Jagannathan Sarangapani Download PDF EPUB FB2

Examining neurocontroller design in discrete-time for the first time, Neural Network Control of Nonlinear Discrete-Time Systems presents powerful modern control techniques based on the parallelism and adaptive capabilities of biological nervous by: Examining neurocontroller design in discrete-time for the first time, Neural Network Control of Nonlinear Discrete-Time Systems presents powerful modern control techniques based on the parallelism and adaptive capabilities of biological nervous systems/5(3).

Qin C, Zhang H, Wang Y and Luo Y () Neural network-based online H∞ control for discrete-time affine nonlinear system using adaptive dynamic programming, Neurocomputing, C, (), Online publication date: Jul Neural network control of nonlinear discrete-time systems.

Intelligent systems are a hallmark of modern feedback control systems. But as these systems mature, we have come to expect higher levels of performance in speed and accuracy in the face of severe nonlinearities, disturbances, unforeseen dynamics, and unstructured uncertainties.

Artificial neural networks offer a combination of adaptability, parallel processing, and learning capabilities that outperform other intelligent control. Examining neurocontroller design in discrete-time for the first time, "Neural Network Control of Nonlinear Discrete-Time Systems" presents powerful modern control techniques based on the parallelism and adaptive capabilities of biological nervous : Sarangapani, Jagannathan.

First, a Recurrent High Order Neural Network (RHONN) is used to identify discrete-time unknown nonlinear delayed systems under uncertainties, then a RHONN is used to design neural observers for the same class of systems.

Neural Networks Modelling and Control: Applications for Unknown Nonlinear Delayed Systems in Discrete Time focuses on modeling and control of discrete-time unknown nonlinear delayed systems under uncertainties based on Artificial Neural Networks. First, a Recurrent High Order Neural Network (RHONN) is used to identify discrete-time unknown nonlinear delayed systems under.

Request PDF | Neural network control of nonlinear discrete-time systems | Intelligent systems are a hallmark of modern feedback control systems. But as these systems. Neural network controllers are derived for robot manipulators in a variety of applications including position control, force control, link flexibility stabilization and the management of high-frequency joint and motor dynamics.

The first chapter provides a background on neural networks and the second on dynamical systems and control. Therefore, first, a novel NNCS representation incorporating the system uncertainties and network imperfections are derived in this chapter.

Subsequently, an online neural network (NN) identifier is developed to identify the control coefficient matrix of the stochastic nonlinear discrete-time system for the purpose of the controller design. After providing the background on neural networks and discrete-time adaptive control, he presents chapters discussing neural network control of nonlinear systems and feedback linearization, neural network control of uncertain nonlinear discrete-time systems with actuator nonlinearities, output feedback control of strict feedback nonlinear.

"Neural Network Control of Nonlinear Discrete-Time Systems" by Jagannathan Sarangapani Control Engineering Series. A Series of Reference Books and Textbooks Informa, CRCPs, TFG | | ISBN: | pages | PDF | 12 MB This book presents powerful modern control techniques based on the parallelism and adaptive capabilities of biological nervous systems.

A unifying framework for neuro-control design is presented to view neural network training as a nonlinear optimization problem. This chapter then outlines a new neuro-control concept, referred to as parameterized neuro-nontroller (PNC) and discusses the optimization complexities it poses.

Neural Network Control of Nonlinear Discrete Time Systems presents powerful modern control techniques based on the parallelism and adaptive capabilities of biological nervous systems. Features: Presents the first comprehensive treatise on neurocontroller design in discrete-time.

Neural Network Control of Nonlinear Discrete-Time Systems (Public Administration and Public Policy) Book Title:Neural Network Control of Nonlinear Discrete-Time Systems (Public Administration. After an introduction to neural networks, dynamical systems, control of nonlinear systems, and feedback linearization, the book builds systematically from actuator nonlinearities and strict feedback in nonlinear systems to nonstrict feedback, system identification, model reference adaptive control, and novel optimal control using the Hamilton-Jacobi-Bellman formulation.

Adaptive Sliding Mode Neural Network Control for Nonlinear Systems introduces nonlinear systems basic knowledge, analysis and control methods, and applications in various fields.

It offers instructive examples and simulations, along with the source codes, and provides the basic architecture of control science and engineering. Control of Nonaffine Nonlinear Discrete-Time Systems Using Reinforcement-Learning-Based Linearly Parameterized Neural Networks Abstract: A nonaffine discrete-time system represented by the nonlinear autoregressive moving average with eXogenous input (NARMAX) representation with unknown nonlinear system dynamics is by: Neural network controllers are derived for robot manipulators in a variety of applications including position control, force control, link flexibility stabilization and the management of high-frequency joint and motor first chapter provides a background on neural networks and the second on dynamical systems and control.5/5(1).

"Examining neurocontroller design in discrete-time for the first time, Neural Network Control of Nonlinear Discrete-Time Systems presents powerful modern control techniques based on the parallelism and adaptive capabilities of biological nervous systems. Layered neural networks are used in a nonlinear self-tuning adaptive control problem.

The plant is an unknown feedback-linearizable discrete-time system, r Adaptive control of a class of nonlinear discrete-time systems using neural networks - IEEE Journals & Magazine Skip to Main ContentCited by: In this paper, we propose a novel adaptive dynamic programming (ADP) scheme based on general value iteration to obtain near optimal control for discrete-time nonlinear systems with continuous state and control space.

First, the selection of initial value function is different from the traditional value iteration, and a new method is introduced to demonstrate the convergence property and.In this section, we present results of simulations of adaptive control nonlinear discrete-time systems by using OS-ELM neural networks.

The nonlinear systems will be considered as below: where we define and ; is a scalar. The control goal is to force the system states to track a reference model by: