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کنترل نیروی بازوی یک ربات با استفاده از جبران سازهای عصبی - فازی در چهارچوب امپدانس کنترل
خرمن دار، نگار Kharmandar, Negar
Cataloging brief
کنترل نیروی بازوی یک ربات با استفاده از جبران سازهای عصبی - فازی در چهارچوب امپدانس کنترل
پدیدآور اصلی :
خرمن دار، نگار Kharmandar, Negar
ناشر :
صنعتی شریف
سال انتشار :
1390
موضوع ها :
کنترل کننده فازی Fuzzy Controller کنترل امپدانس Impedance Control کنترل کننده تناسبی -...
شماره راهنما :
58-41963
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Chapter 1
(12)
1.1 Control of robots in contact tasks
(12)
1.2 Classification of constraint motion
(13)
In recent years different control strategies have been proposed on the problem of controlling the compliant motion. Various cr
(13)
1.2.1 Passive compliance
(14)
1.2.2 Active compliance
(14)
1.2.2.1 Hybrid position/force control
(14)
1.2.2.2 Impedance control
(15)
1.3 Objective of the study
(20)
1.4 Organization of thesis
(21)
1.5 Summary
(21)
Chapter 2
(22)
2.1 Introduction
(22)
2.2 What is impedance control?
(22)
2.2.1 Torque-based impedance control
(26)
2.2.2 Position-based impedance control
(27)
2.3 Impedance control and force tracking
(28)
2.4 Review of impedance control and force tracking
(29)
2.5 Summary
(33)
Chapter 3
(34)
The Electrical PUMA560 Robot
(34)
3.1 Description of manipulator
(34)
3.2 Kinematics of PUMA 560
(35)
where, .
(40)
3.3 Dynamic model of PUMA 560
(40)
3.3.1 Dynamic characteristic of PUMA 560
(41)
3.4 PUMA 560 actuation system
(42)
3.5 Simulation program
(45)
3.6 Summary
(45)
Chapter 4
(46)
The Application of Position-Based Impedance Control to PUMA 560 Manipulator
(46)
4.1 Introduction
(46)
4.2 Position-Based Impedance Control structure
(46)
4.3 Nonlinear PID position controller
(49)
4.3.1 Applying conventional PID controllers
(49)
4.3.2 Modified rate-varying integral
(52)
Chapter 5
(63)
5.1 Introduction
(63)
5.2 Force tracking in impedance control
(63)
5.3 Neuro-Fuzzy system
(66)
5.3.1 Fuzzy system
(67)
5.3.2 Neural Network
(68)
5.3.3 Adaptive Network-based Fuzzy Inference System (ANFIS) architecture
(69)
5.3.3.1 Basic learning rule
(70)
5.3.4 Architecture of ANFIS
(72)
5.3.5 Hybrid learning procedure for ANFIS
(74)
5.4 Applying the ANFIS to the PBIC for the purpose of force tracking
(77)
5.5 Comparison of the proposed method with the previous works
(90)
5.6 Summary
(92)
Chapter 6
(93)