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Differential Neural Networks for Robust Nonlinear Control : Identification, State Estimation and Trajectory Tracking

Identification, State Estimation and Trajectory Tracking

By: Alex Poznyak, Edgar N. Sanchez, Wen Yu

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Hardcover | 1 December 2000

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Hardcover
456 Pages

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21.84 x 15.75 x 2.79

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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).

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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
Some Useful Mathematical Facts
Elements of Qualitative Theory of ODE
Locally Optimal Control and Optimization
Table of Contents provided by Blackwell. All Rights Reserved.

Differential Neural Networks for Robust Nonlinear Control : Identification, State Estimation and Trajectory Tracking

Identification, State Estimation and Trajectory Tracking

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Hardcover

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