๐”– Scriptorium
โœฆ   LIBER   โœฆ

๐Ÿ“

DIFFERENTIAL NEURAL NETWORKS FOR ROBUST NONLINEAR CONTROL

โœ Scribed by Alexander S. Poznyak, Edgar N. Sanchez, Wen Yu


Publisher
World Scientific Publishing Company
Year
2001
Tongue
English
Leaves
454
Edition
1st
Category
Library

โฌ‡  Acquire This Volume

No coin nor oath required. For personal study only.

โœฆ Synopsis


This volume 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).


๐Ÿ“œ SIMILAR VOLUMES


Differential Neural Networks for Robust
โœ Alexander S. Poznyak, Edgar N. Sanchez, Wen Yu ๐Ÿ“‚ Library ๐Ÿ“… 2001 ๐Ÿ› World Scientific Publishing Company ๐ŸŒ English

This volume 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

Adaptive Sliding Mode Neural Network Con
โœ Yang Li, Jianhua Zhang, Qiong Wu ๐Ÿ“‚ Library ๐Ÿ“… 2018 ๐Ÿ› Academic Press ๐ŸŒ 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

Robust Receding Horizon Control for Netw
โœ Huiping Li, Yang Shi (auth.) ๐Ÿ“‚ Library ๐Ÿ“… 2017 ๐Ÿ› Springer International Publishing ๐ŸŒ English

<p><p>This book offers a comprehensive, easy-to-understand overview of receding-horizon control for nonlinear networks. It presents novel general strategies that can simultaneously handle general nonlinear dynamics, system constraints, and disturbances arising in networked and large-scale systems an

Neural networks for control
โœ W Thomas Miller; Richard S Sutton; Paul J Werbos; National Science Foundation ( ๐Ÿ“‚ Library ๐Ÿ“… 1990 ๐Ÿ› MIT Press ๐ŸŒ English