𝔖 Scriptorium
✦   LIBER   ✦

πŸ“

Neural Network-Based Adaptive Control of Uncertain Nonlinear Systems

✍ Scribed by Kasra Esfandiari, Farzaneh Abdollahi, Heidar A. Talebi


Publisher
Springer
Year
2021
Tongue
English
Leaves
181
Edition
1st ed. 2022
Category
Library

⬇  Acquire This Volume

No coin nor oath required. For personal study only.

✦ Synopsis


The focus of this book is the application of artificial neural networks in uncertain dynamical systems. It explains how to use neural networks in concert with adaptive techniques for system identification, state estimation, and control problems. The authors begin with a brief historical overview of adaptive control, followed by a review of mathematical preliminaries. In the subsequent chapters, they present several neural network-based control schemes. Each chapter starts with a concise introduction to the problem under study, and a neural network-based control strategy is designed for the simplest case scenario. After these designs are discussed, different practical limitations (i.e., saturation constraints and unavailability of all system states) are gradually added, and other control schemes are developed based on the primary scenario. Through these exercises, the authors present structures that not only provide mathematical tools for navigating control problems, but also supply solutions that are pertinent to real-life systems.
Β 

✦ Table of Contents


Preface
Contents
Acronyms
List of Figures
List of Tables
1 Introduction
1.1 Nonlinear Adaptive Control: Motivation and Overview
1.2 Outline
1.2.1 Chapter 2: Mathematical Preliminaries
1.2.2 Chapter 3: NN-Based Adaptive Control of Affine Nonlinear Systems
1.2.3 Chapter 4: NN-Based Adaptive Control of Nonaffine Canonical Nonlinear Systems
1.2.4 Chapter 5: NN-Based Adaptive Control of Nonaffine Noncanonical Nonlinear Systems
1.2.5 Chapter 6: NN-Based Adaptive Control of MIMO Nonaffine Noncanonical Nonlinear Systems
References
2 Mathematical Preliminaries
2.1 Mathematical Review
2.2 Geometric Nonlinear Control
2.3 Multilayer Neural Networks
References
3 NN-Based Adaptive Control of Affine Nonlinear Systems
3.1 Introduction
3.2 State Feedback Control of Affine Nonlinear Systems
3.2.1 Problem Formulation
3.2.2 Adaptive NN-Based Controller Design
3.2.3 Stability Analysis
3.2.4 Simulation Results
3.2.4.1 Van der Pol Oscillator Nonlinear System
3.2.4.2 Pendulum Nonlinear System
3.3 State Feedback Control of Affine Nonlinear Systems with Input Constraints
3.3.1 Problem Formulation
3.3.2 Adaptive NN-Based Controller Design
3.3.3 Stability Analysis
3.3.4 Simulation Results
3.3.4.1 Van der Pol Oscillator Nonlinear System
3.3.4.2 Pendulum Nonlinear System
3.4 Output Feedback Control of Affine Nonlinear Systems with Input Constraints
3.4.1 Problem Formulation
3.4.2 Adaptive NN-Based Controller Design
3.4.3 Stability Analysis
3.4.4 Simulation Results
3.4.4.1 Van der Pol Oscillator Nonlinear System
3.4.4.2 Pendulum Nonlinear System
3.5 Conclusions
References
4 NN-Based Adaptive Control of Nonaffine Canonical Nonlinear Systems
4.1 Introduction
4.2 State Feedback Control of Nonaffine Nonlinear Systems
4.2.1 Problem Formulation
4.2.2 Adaptive NN-Based Controller Design
4.2.3 Stability Analysis
4.2.4 Simulation Results
4.2.4.1 Pendulum Nonlinear System
4.2.4.2 Duffing–Holmes Chaotic System
4.2.4.3 Numerical Example
4.3 State Feedback Control of Nonaffine Nonlinear Systems with Input Constraints
4.3.1 Problem Formulation
4.3.2 Adaptive NN-Based Controller Design
4.3.3 Stability Analysis
4.3.4 Simulation Results
4.3.4.1 Pendulum Nonlinear System
4.3.4.2 Duffing–Holmes Chaotic System
4.3.4.3 Numerical Example
4.4 Output Feedback Control of Nonaffine Nonlinear Systems with Input Constraints
4.4.1 Problem Formulation
4.4.2 Adaptive NN-Based Controller Design
4.4.3 Stability Analysis
4.4.4 Simulation Results
4.4.4.1 Pendulum Nonlinear System
4.4.4.2 Duffing–Holmes Chaotic System
4.4.4.3 Numerical Example
4.5 Conclusions
References
5 NN-Based Adaptive Control of Nonaffine Noncanonical Nonlinear Systems
5.1 Introduction
5.2 State Feedback Control of Nonaffine Systems with Unknown Control Direction
5.2.1 Problem Formulation
5.2.2 Adaptive NN-Based Controller Design
5.2.3 Stability Analysis
5.2.4 Simulation Results
5.3 Output Feedback Control of Nonaffine Systems with Unknown Control Direction
5.3.1 Problem Formulation
5.3.2 Adaptive NN-Based Controller Design
5.3.3 Stability Analysis
5.3.4 Simulation Results
5.4 Conclusions
References
6 NN-Based Adaptive Control of MIMO Nonaffine Noncanonical Nonlinear Systems
6.1 Introduction
6.2 Problem Formulation
6.3 Adaptive NN-Based Controller Design
6.4 Stability Analysis
6.5 Simulation Results
6.5.1 Numerical System
6.5.2 Case Study 1: Application to Flexible Link Manipulators
6.5.2.1 Two-Link Flexible Manipulator Model
6.5.2.2 Simulation Results
6.5.3 Case Study 2: Experimental Results on Whole Arm Manipulator
6.6 Conclusions
References
Index


πŸ“œ SIMILAR VOLUMES


Neural Network-Based Adaptive Control of
✍ Kasra Esfandiari, Farzaneh Abdollahi, Heidar A. Talebi πŸ“‚ Library πŸ“… 2021 πŸ› Springer 🌐 English

<div>The focus of this book is the application of artificial neural networks in uncertain dynamical systems. It explains how to use neural networks in concert with adaptive techniques for system identification, state estimation, and control problems. The authors begin with a brief historical overvie

Adaptive Sliding Mode Neural Network Con
✍ Yang Li, Jianhua Zhang, Qiong Wu πŸ“‚ Library πŸ“… 2018 πŸ› Academic Press 🌐 English

<p><span>Adaptive Sliding Mode Neural Network Control for Nonlinear Systems</span><span> 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

Neural network control of nonlinear disc
✍ Jagannathan Sarangapani πŸ“‚ Library πŸ“… 2006 πŸ› CRC/Taylor & Francis 🌐 English

No fluff here, this book is chock full of valuable and insightful information on the application of recurrent closed loop neural nets for estimating and controlling nonlinear time series.

Neural network control of nonlinear disc
✍ Jagannathan Sarangapani πŸ“‚ Library πŸ“… 2006 πŸ› CRC/Taylor & Francis 🌐 English

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 net

Neural Network Control of Nonlinear Disc
✍ Jagannathan Sarangapani πŸ“‚ Library πŸ“… 2006 πŸ› CRC Press 🌐 English

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 net