Neural Network Control Of Robot Manipulators And Non-Linear Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Neural Network Control Of Robot Manipulators And Non-Linear Systems write by F W Lewis. This book was released on 1998-11-30. Neural Network Control Of Robot Manipulators And Non-Linear Systems available in PDF, EPUB and Kindle. 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 - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Neural Network Control Of Robot Manipulators And Non-Linear Systems write by F W Lewis. This book was released on 2020-08-14. Neural Network Control Of Robot Manipulators And Non-Linear Systems available in PDF, EPUB and Kindle. 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
Adaptive Neural Network Control of Robotic Manipulators - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Adaptive Neural Network Control of Robotic Manipulators write by Tong Heng Lee. This book was released on 1998. Adaptive Neural Network Control of Robotic Manipulators available in PDF, EPUB and Kindle. 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
Differential Neural Networks for Robust Nonlinear Control - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Differential Neural Networks for Robust Nonlinear Control write by Alexander S. Poznyak. This book was released on 2001. Differential Neural Networks for Robust Nonlinear Control available in PDF, EPUB and Kindle. 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.
Neural Network-Based State Estimation of Nonlinear Systems
Neural Network-Based State Estimation of Nonlinear Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Neural Network-Based State Estimation of Nonlinear Systems write by Heidar A. Talebi. This book was released on 2009-12-04. Neural Network-Based State Estimation of Nonlinear Systems available in PDF, EPUB and Kindle. "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.
