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Neural Network Control Of Robot Manipulators And Non-Linear Systems

1998-11-30 Technology & Engineering
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Release : 1998-11-30
Genre : Technology & Engineering
Kind : eBook
Book Rating : 961/5 ( reviews)

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Book Synopsis Neural Network Control Of Robot Manipulators And Non-Linear Systems by : F W Lewis

Download or read book Neural Network Control Of Robot Manipulators And Non-Linear Systems written by F W Lewis. This book was released on 1998-11-30. Available in PDF, EPUB and Kindle. Book excerpt: 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 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. Chapter three introduces the robot control problem and standard techniques such as torque, adaptive and robust control. Subsequent chapters give design techniques and Stability Proofs For NN Controllers For Robot Arms, Practical Robotic systems with high frequency vibratory modes, force control and a general class of non-linear systems. The last chapters are devoted to discrete- time NN controllers. Throughout the text, worked examples are provided.

Neural Network Control Of Robot Manipulators And Non-Linear Systems

2020-08-14 Technology & Engineering
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Release : 2020-08-14
Genre : Technology & Engineering
Kind : eBook
Book Rating : 77X/5 ( reviews)

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Book Synopsis Neural Network Control Of Robot Manipulators And Non-Linear Systems by : F W Lewis

Download or read book Neural Network Control Of Robot Manipulators And Non-Linear Systems written by F W Lewis. This book was released on 2020-08-14. Available in PDF, EPUB and Kindle. Book excerpt: 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 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. Chapter three introduces the robot control problem and standard techniques such as torque, adaptive and robust control. Subsequent chapters give design techniques and Stability Proofs For NN Controllers For Robot Arms, Practical Robotic systems with high frequency vibratory modes, force control and a general class of non-linear systems. The last chapters are devoted to discrete- time NN controllers. Throughout the text, worked examples are provided.

Adaptive Neural Network Control of Robotic Manipulators

1998
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Release : 1998
Genre :
Kind : eBook
Book Rating : 522/5 ( reviews)

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Book Synopsis Adaptive Neural Network Control of Robotic Manipulators by : Tong Heng Lee

Download or read book Adaptive Neural Network Control of Robotic Manipulators written by Tong Heng Lee. This book was released on 1998. Available in PDF, EPUB and Kindle. Book excerpt: Introduction; Mathematical background; Dynamic modelling of robots; Structured network modelling of robots; Adaptive neural network control of robots; Neural network model reference adaptive control; Flexible joint robots; task space and force control; Bibliography; Computer simulation; Simulation software in C.

Differential Neural Networks for Robust Nonlinear Control

2001 Science
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Release : 2001
Genre : Science
Kind : eBook
Book Rating : 295/5 ( reviews)

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Book Synopsis Differential Neural Networks for Robust Nonlinear Control by : Alexander S. Poznyak

Download or read book Differential Neural Networks for Robust Nonlinear Control written by Alexander S. Poznyak. This book was released on 2001. Available in PDF, EPUB and Kindle. Book excerpt: 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.

High-level Feedback Control With Neural Networks

1998-09-28 Technology & Engineering
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Release : 1998-09-28
Genre : Technology & Engineering
Kind : eBook
Book Rating : 456/5 ( reviews)

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Book Synopsis High-level Feedback Control With Neural Networks by : Young Ho Kim

Download or read book High-level Feedback Control With Neural Networks written by Young Ho Kim. This book was released on 1998-09-28. Available in PDF, EPUB and Kindle. Book excerpt: Complex industrial or robotic systems with uncertainty and disturbances are difficult to control. As system uncertainty or performance requirements increase, it becomes necessary to augment traditional feedback controllers with additional feedback loops that effectively “add intelligence” to the system. Some theories of artificial intelligence (AI) are now showing how complex machine systems should mimic human cognitive and biological processes to improve their capabilities for dealing with uncertainty.This book bridges the gap between feedback control and AI. It provides design techniques for “high-level” neural-network feedback-control topologies that contain servo-level feedback-control loops as well as AI decision and training at the higher levels. Several advanced feedback topologies containing neural networks are presented, including “dynamic output feedback”, “reinforcement learning” and “optimal design”, as well as a “fuzzy-logic reinforcement” controller. The control topologies are intuitive, yet are derived using sound mathematical principles where proofs of stability are given so that closed-loop performance can be relied upon in using these control systems. Computer-simulation examples are given to illustrate the performance.

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