Differential Neural Networks for Robust Nonlinear Control

This book PDF is perfect for those who love Science genre, written by Alexander S. Poznyak and published by World Scientific which was released on 03 May 2024 with total hardcover pages 464. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Differential Neural Networks for Robust Nonlinear Control books below.

Differential Neural Networks for Robust Nonlinear Control
Differential Neural Networks for Robust Nonlinear Control
Author : Alexander S. Poznyak
File Size : 40,6 Mb
Publisher : World Scientific
Language : English
Release Date : 03 May 2024
ISBN : 981281129X
Pages : 464 pages
Get Book

Differential Neural Networks for Robust Nonlinear Control by Alexander S. Poznyak Book PDF Summary

This book deals with continuous time dynamic neural networks theory applied to the solution of basic problems in robust control theory, including identification, state space estimation (based on neuro-observers) and trajectory tracking. The plants to be identified and controlled are assumed to be a priori unknown but belonging to a given class containing internal unmodelled dynamics and external perturbations as well. The error stability analysis and the corresponding error bounds for different problems are presented. The effectiveness of the suggested approach is illustrated by its application to various controlled physical systems (robotic, chaotic, chemical, etc.). Contents: Theoretical Study: Neural Networks Structures; Nonlinear System Identification: Differential Learning; Sliding Mode Identification: Algebraic Learning; Neural State Estimation; Passivation via Neuro Control; Neuro Trajectory Tracking; Neurocontrol Applications: Neural Control for Chaos; Neuro Control for Robot Manipulators; Identification of Chemical Processes; Neuro Control for Distillation Column; General Conclusions and Future Work; Appendices: Some Useful Mathematical Facts; Elements of Qualitative Theory of ODE; Locally Optimal Control and Optimization. Readership: Graduate students, researchers, academics/lecturers and industrialists in neural networks.

Differential Neural Networks for Robust Nonlinear Control

Differential Neural Networks for Robust Nonlinear Control by Alexander S. Poznyak,Edgar N. Sanchez,Wen Yu (profesor titular.)

This book deals with continuous time dynamic neural networks theory applied to the solution of basic problems in robust control theory, including identification, state space estimation (based on neuro-observers) and trajectory tracking. The plants to be identified and controlled are assumed to be a priori unknown but belonging to a

Get Book
Neural Network Based Robust Nonlinear Control

Neural Network Based Robust Nonlinear Control by Nishant Unnikrishnan

"Online trained neural networks have become popular in recent years in the design of robust and adaptive controllers for dynamic systems with uncertainties due to their universal function approximation capabilities. This research explores the application of online neural networks for the design of model following controllers and for dynamic reoptimization

Get Book
Advances in Neural Networks ISNN 2004

Advances in Neural Networks ISNN 2004 by Fuliang Yin,Jun Wang,Chengan Guo

This book constitutes the proceedings of the International Symposium on Neural N- works (ISNN 2004) held in Dalian, Liaoning, China duringAugust 19–21, 2004. ISNN 2004 received over 800 submissions from authors in ?ve continents (Asia, Europe, North America, South America, and Oceania), and 23 countries and regions (mainland China, Hong Kong, Taiwan, South Korea, Japan, Singapore,

Get Book
Nonlinear H2 H Infinity Constrained Feedback Control

Nonlinear H2 H Infinity Constrained Feedback Control by Murad Abu-Khalaf,Jie Huang,Frank L. Lewis

This book provides techniques to produce robust, stable and useable solutions to problems of H-infinity and H2 control in high-performance, non-linear systems for the first time. The book is of importance to control designers working in a variety of industrial systems. Case studies are given and the design of nonlinear

Get Book
Robust Nonlinear Control Design

Robust Nonlinear Control Design by Randy Freeman,Petar V. Kokotovic

This softcover book summarizes Lyapunov design techniques for nonlinear systems and raises important issues concerning large-signal robustness and performance. The authors have been the first to address some of these issues, and they report their findings in this text. The researcher who wishes to enter the field of robust nonlinear

Get Book
Robust and Fault Tolerant Control

Robust and Fault Tolerant Control by Krzysztof Patan

Robust and Fault-Tolerant Control proposes novel automatic control strategies for nonlinear systems developed by means of artificial neural networks and pays special attention to robust and fault-tolerant approaches. The book discusses robustness and fault tolerance in the context of model predictive control, fault accommodation and reconfiguration, and iterative learning control

Get Book
Artificial Higher Order Neural Networks for Modeling and Simulation

Artificial Higher Order Neural Networks for Modeling and Simulation by Zhang, Ming

"This book introduces Higher Order Neural Networks (HONNs) to computer scientists and computer engineers as an open box neural networks tool when compared to traditional artificial neural networks"--Provided by publisher.

Get Book
Neural Network Control of Nonlinear Discrete Time Systems

Neural Network Control of Nonlinear Discrete Time Systems by Jagannathan Sarangapani

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,

Get Book