Neural Network Control Of Robot Manipulators And Non-Linear Systems
Neural Network Control Of Robot Manipulators And Non-Linear Systems

Author: F W Lewis

Publisher: CRC Press

Published: 1998-11-30

Total Pages: 470

ISBN-13: 9780748405961

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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
Neural Network Control Of Robot Manipulators And Non-Linear Systems

Author: F W Lewis

Publisher: CRC Press

Published: 2020-08-14

Total Pages: 468

ISBN-13: 100016277X

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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-Based State Estimation of Nonlinear Systems
Neural Network-Based State Estimation of Nonlinear Systems

Author: Heidar A. Talebi

Publisher: Springer

Published: 2009-12-04

Total Pages: 166

ISBN-13: 1441914382

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"Neural Network-Based State Estimation of Nonlinear Systems" presents efficient, easy to implement neural network schemes for state estimation, system identification, and fault detection and Isolation with mathematical proof of stability, experimental evaluation, and Robustness against unmolded dynamics, external disturbances, and measurement noises.

Differential Neural Networks for Robust Nonlinear Control
Differential Neural Networks for Robust Nonlinear Control

Author: Alexander S. Poznyak

Publisher: World Scientific

Published: 2001

Total Pages: 455

ISBN-13: 9810246242

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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.).

Adaptive Neural Network Control of Robotic Manipulators
Adaptive Neural Network Control of Robotic Manipulators

Author: Tong Heng Lee

Publisher: World Scientific

Published: 1998

Total Pages: 400

ISBN-13: 9789810234522

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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.

Neural Network Control of Nonlinear Discrete-Time Systems
Neural Network Control of Nonlinear Discrete-Time Systems

Author: Jagannathan Sarangapani

Publisher: CRC Press

Published: 2018-10-03

Total Pages: 624

ISBN-13: 1420015451

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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, and learning capabilities that outperform other intelligent control methods in more complex systems. Borrowing from Biology Examining neurocontroller design in discrete-time for the first time, Neural Network Control of Nonlinear Discrete-Time Systems presents powerful modern control techniques based on the parallelism and adaptive capabilities of biological nervous systems. At every step, the author derives rigorous stability proofs and presents simulation examples to demonstrate the concepts. Progressive Development After an introduction to neural networks, dynamical systems, control of nonlinear systems, and feedback linearization, the book builds systematically from actuator nonlinearities and strict feedback in nonlinear systems to nonstrict feedback, system identification, model reference adaptive control, and novel optimal control using the Hamilton-Jacobi-Bellman formulation. The author concludes by developing a framework for implementing intelligent control in actual industrial systems using embedded hardware. Neural Network Control of Nonlinear Discrete-Time Systems fosters an understanding of neural network controllers and explains how to build them using detailed derivations, stability analysis, and computer simulations.

Nonlinear Control of Robots and Unmanned Aerial Vehicles
Nonlinear Control of Robots and Unmanned Aerial Vehicles

Author: Ranjan Vepa

Publisher: CRC Press

Published: 2016-10-14

Total Pages: 563

ISBN-13: 1498767052

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Nonlinear Control of Robots and Unmanned Aerial Vehicles: An Integrated Approach presents control and regulation methods that rely upon feedback linearization techniques. Both robot manipulators and UAVs employ operating regimes with large magnitudes of state and control variables, making such an approach vital for their control systems design. Numerous application examples are included to facilitate the art of nonlinear control system design, for both robotic systems and UAVs, in a single unified framework. MATLAB® and Simulink® are integrated to demonstrate the importance of computational methods and systems simulation in this process.

Adaptive Neural Network Control of Robotic Manipulators
Adaptive Neural Network Control of Robotic Manipulators

Author: Shuzhi S. Ge

Publisher: World Scientific Series In Robotics And Intelligent Systems

Published: 1998

Total Pages: 381

ISBN-13: 9789810234522

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Recently, there has been considerable research interest in neural network control of robots, and satisfactory results have been obtained in solving some of the special issues associated with the problems of robot control in an "on-and-off" fashion. This book is dedicated to issues on adaptive control of robots based on neural networks. The text has been carefully tailored to (i) give a comprehensive study of robot dynamics, (ii) present structured network models for robots, and (iii) provide systematic approaches for neural network based adaptive controller design for rigid robots, flexible joint robots, and robots in constraint motion. Rigorous proof of the stability properties of adaptive neural network controllers is provided. Simulation examples are also presented to verify the effectiveness of the controllers, and practical implementation issues associated with the controllers are also discussed.

Decentralized Neural Control: Application to Robotics
Decentralized Neural Control: Application to Robotics

Author: Ramon Garcia-Hernandez

Publisher: Springer

Published: 2017-02-05

Total Pages: 121

ISBN-13: 3319533126

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This book provides a decentralized approach for the identification and control of robotics systems. It also presents recent research in decentralized neural control and includes applications to robotics. Decentralized control is free from difficulties due to complexity in design, debugging, data gathering and storage requirements, making it preferable for interconnected systems. Furthermore, as opposed to the centralized approach, it can be implemented with parallel processors. This approach deals with four decentralized control schemes, which are able to identify the robot dynamics. The training of each neural network is performed on-line using an extended Kalman filter (EKF). The first indirect decentralized control scheme applies the discrete-time block control approach, to formulate a nonlinear sliding manifold. The second direct decentralized neural control scheme is based on the backstepping technique, approximated by a high order neural network. The third control scheme applies a decentralized neural inverse optimal control for stabilization. The fourth decentralized neural inverse optimal control is designed for trajectory tracking. This comprehensive work on decentralized control of robot manipulators and mobile robots is intended for professors, students and professionals wanting to understand and apply advanced knowledge in their field of work.