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Fusion of Hard and Soft Control Strategies for the Robotic Hand [Kõva köide]

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Püsilink: https://www.kriso.ee/db/9781119273592.html
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An in-depth review of hybrid control techniques for smart prosthetic hand technology by two of the worlds pioneering experts in the field

Long considered the stuff of science fiction, a prosthetic hand capable of fully replicating all of that appendages various functions is closer to becoming reality than ever before. This book provides a comprehensive report on exciting recent developments in hybrid control techniquesone of the most crucial hurdles to be overcome in creating smart prosthetic hands.

Coauthored by two of the worlds foremost pioneering experts in the field, Fusion of Hard and Soft Control Strategies for Robotic Hand treats robotic hands for multiple applications. Itbegins withan overview of advances in main control techniques that have been made over the past decade before addressing the military context for affordable robotic hand technology with tactile and/or proprioceptive feedback for hand amputees. Kinematics, homogeneous transformations, inverse and differential kinematics, trajectory planning, and dynamic models of two-link thumb and three-link index finger are discussed in detail. The remainder of the book is devoted to the most promising soft computing techniques, particle swarm optimization techniques, and strategies combining hard and soft controls.

In addition, the book:





Includes a report on exciting new developments in prosthetic/robotic hand technology, with an emphasis on the fusion of hard and soft control strategies Covers both prosthetic and non-prosthetic hand designs for everything from routine human operations, robotic surgery, and repair and maintenance, to hazardous materials handling, space applications, explosives disposal, and more Provides a comprehensive overview of five-fingered robotic hand technology kinematics, dynamics, and control Features detailed coverage of important recent developments in neuroprosthetics

Fusion of Hard and Soft Control Strategies for Robotic Hand is a must-read for researchers in control engineering, robotic engineering, biomedical sciences and engineering, and rehabilitation engineering.
List of Figures
xi
List of Tables
xvii
1 Introduction
1(46)
1.1 Relevance to Military
2(1)
1.2 Control Strategies
3(16)
1.2.1 Prosthetic/Robotic Hands
3(2)
1.2.2 Chronological Overview
5(10)
1.2.3 Overview of Main Control Techniques Since 2007
15(3)
1.2.4 Revolutionary Prosthesis
18(1)
1.3 Fusion of Intelligent Control Strategies
19(3)
1.3.1 Fusion of Hard and Soft Computing/Control Strategies
19(3)
1.4 Overview of Our Research
22(1)
1.5 Developments in Neuroprosthetics
23(1)
1.6
Chapter Summary
24(23)
2 Kinematics and Trajectory Planning
47(46)
2.1 Human Hand Anatomy
48(1)
2.2 Forward Kinematics
49(17)
2.2.1 Homogeneous Transformations
50(4)
2.2.2 Serial n-Link Revolute-Joint Planar Manipulator
54(4)
2.2.3 Two-Link Thumb
58(2)
2.2.4 Three-Link Index Finger
60(2)
2.2.5 Three-Dimensional Five-Fingered Robotic Hand
62(4)
2.3 Inverse Kinematics
66(4)
2.3.1 Two-Link Thumb
66(1)
2.3.2 Three-Link Fingers
67(1)
2.3.3 Fingertip Workspace
68(1)
2.3.3.1 Two-Link Thumb and Three-Link Index Finger
69(1)
2.3.3.2 Five-Fingered Robotic Hand
70(1)
2.4 Differential Kinematics
70(10)
2.4.1 Serial n-Link Revolute-Joint Planar Manipulator
71(1)
2.4.1.1 Some Properties of Rotation Matrices
72(2)
2.4.1.2 Rigid Body Kinematics
74(4)
2.4.2 Two-Link Thumb
78(1)
2.4.3 Three-Link Index Finger
79(1)
2.5 Trajectory Planning
80(13)
2.5.1 Trajectory Planning Using Cubic Polynomial
81(1)
2.5.2 Trajectory Planning Using Cubic Bezier Curve
82(2)
2.5.3 Simulation Results of Trajectory Paths
84(9)
3 Dynamic Models
93(12)
3.1 Actuators
93(3)
3.1.1 Electric DC Motor
93(1)
3.1.2 Mechanical Gear Transmission
94(2)
3.2 Dynamics
96(1)
3.3 Two-Link Thumb
96(3)
3.4 Three-Link Index Finger
99(6)
4 Soft Computing/Control Strategies
105(56)
4.1 Fuzzy Logic
105(3)
4.2 Neural Network
108(1)
4.3 Adaptive Neuro-Fuzzy Inference System
108(5)
4.4 Tabu Search
113(5)
4.4.1 Tabu Concepts
113(1)
4.4.2 Enhanced Continuous Tabu Search
114(1)
4.4.2.1 Initialization of Parameters
114(1)
4.4.2.2 Diversification
114(1)
4.4.2.3 Selecting the Most Promising Area
115(1)
4.4.2.4 Intensification
116(2)
4.5 Genetic Algorithm
118(3)
4.5.1 Basic GA Procedures
118(3)
4.6 Particle Swarm Optimization
121(9)
4.6.1 Basic PSO Procedures and Formulations
121(4)
4.6.2 Five Different PSO Techniques
125(3)
4.6.3 Uniform Distribution and Normal Distribution
128(2)
4.7 Adaptive Particle Swarm Optimization
130(6)
4.7.1 APSO Procedures and Formulations
130(4)
4.7.2 Changed/Unchanged Velocity Direction
134(2)
4.8 Condensed Hybrid Optimization
136(1)
4.9 Simulation Results and Discussion
137(24)
4.9.1 PSO Dynamics Investigation
137(1)
4.9.1.1 Benchmark Problems
137(1)
4.9.1.2 Selection of Parameters
138(1)
4.9.1.3 Simulations
139(6)
4.9.2 APSO to Multiple Dimensional Problems
145(4)
4.9.3 PSO in Other Biomedical Applications
149(1)
4.9.3.1 Leukocyte Adhesion Molecules Modeling
149(2)
4.9.4 CHO to Multiple Dimensional Problems
151(10)
5 Fusion of Hard and Soft Control Strategies I
161(42)
5.1 Feedback Linearization
161(2)
5.1.1 State Variable Representation
162(1)
5.2 PD/PI/PID Controllers
163(4)
5.2.1 PD Controller
164(1)
5.2.2 PI Controller
165(1)
5.2.3 PID Controller
165(2)
5.3 Optimal Controller
167(3)
5.3.1 Optimal Regulation
167(1)
5.3.2 Linear Quadratic Optimal Control with Tracking System
167(1)
5.3.3 A Modified Optimal Control with Tracking System
168(2)
5.4 Adaptive Controller
170(2)
5.5 Simulation Results and Discussion
172(26)
5.5.1 Two-Link Thumb
172(3)
5.5.2 Three-Link Index Finger
175(2)
5.5.3 Three-Dimensional Five-Fingered Robotic Hand
177(1)
5.5.3.1 PID Control
177(1)
5.5.3.2 Optimal Control
178(20)
5.4 Appendix: Regression Matrix
198(5)
6 Fusion of Hard and Soft Control Strategies II
203(20)
6.1 Fuzzy-Logic-Based PD Fusion Control Strategy
203(9)
6.1.1 Simulation Results and Discussion
207(5)
6.2 Genetic-Algorithm-Based PID Fusion Control Strategy
212(11)
6.2.1 Simulation Results and Discussion
213(10)
7 Conclusions and Future Work
223(6)
7.1 Conclusions
223(2)
7.2 Future Directions
225(4)
Index 229(2)
Epilogue 231
CHENG-HUNG CHEN, PhD2, IEEE Senior Member, is an Applications Engineer at Synova USA in Secaucus, New Jersey, which is Micro-Machining Center of Laser MicroJet© technology. He performs Laser Micro-machining tests to meet customer expectations in industry markets of aerospace, medical healthcare, diamond & jewelry, energy, tool manufacturing, and semiconductor. He holds doctorates in Engineering and Applied Science as well as in the Biological Sciences.

DESINENI SUBBARAM NAIDU, PhD, Life Fellow IEEE, is the Minnesota Power Jack F. Rowe Endowed Chair and Professor of Electrical Engineering at the University of Minnesota, Duluth, USA. He pioneered several notable interdisciplinary research projects including Smart Prosthetic Hand Technology at the Measurement and Control Engineering Research Center (MCERC), Idaho State University, Pocatello, Idaho, USA.