Flexible Robot Manipulators: Modelling,simulation and control (Control,Robotics and Sensors)
Authors: M.O. Tokhi – A.K.M. Azad
ISBN-10: 1849195838
ISBN-13: 9781849195836
Edition 版本: 2
Released: 2017-06-28
Pages: 536 pages
Book Description
Industrial automation is driving the development of robot manipulators in various applications,with much of the research effort focussed on flexible manipulators and their advantages compared to their rigid counterparts. This book reports recent advances and new developments in the analysis and control of these robot manipulators.
After a general overview of flexible manipulators the book introduces a range of modelling and simulation techniques based on the Lagrange equation formulation,parametric approaches based on linear input/output models using system identification techniques,neuro-modelling approaches,and numerical techniques for dynamic characterisation using finite difference and finite element techniques. Control techniques are then discussed,including a range of open-loop and closed-loop control techniques based on classical and modern control methods including neuro and iterative control,and a range of soft-computing control techniques based on fuzzy logic,neural networks,and evolutionary and bio-inspired optimisation paradigms. Finally the book presents SCEFMAS,a software environment for analysis,design,simulation and control of flexible manipulators.
Flexible Robot Manipulators is essential reading for advanced students of robotics,mechatronics and control engineering and will serve as a source of reference for research in areas of modelling,simulation and control of dynamic flexible structures in general and,specifically,of flexible robotic manipulators.
Contents
Foreword
Abbreviations
Notation
1 Flexible manipulators-an overview
2 Design of a flexible manipulator experimental system
3Dynamic characterisation of a single-link flexible manipulator
4 Finite difference modelling
5 Finite element modelling
6Linear parametric modelling
7 Neural network modelling
8 Open-loop control using command generation techniques
9 Collocated and non-collocated control
10 Hybrid iterative learning control
11 Fuzzy logic control
12 Multi-objective genetic algorithm control
13 Multi-objective particle swarm optimisation control
14 Evolutionary neuro-fuzzy control
15 Software environment for modelling and control of flexible manipulators
References
Index
Back Cover
