Two-stage adaptive robot position/force control using fuzzy reasoning and neural networks

Kazuo Kiguchi, Keigo Watanabe, Kiyotaka Izumi, Toshio Fukuda

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Many studies have been performed on the position/force control of robot manipulators. Since the desired position and force required to realize certain tasks are usually designated in the operational space, the controller should adapt itself to an environment and generate the control force vector in the operational space. On the other hand, the friction of each joint of a robot manipulator is a serious problem since it impedes control accuracy. Therefore, the friction should be effectively compensated for in order to realize precise control of robot manipulators. Recently, soft computing techniques (fuzzy reasoning, neural networks and genetic algorithms) have been playing an important role in the control of robots. Applying the fuzzy-neuro approach (a combination of fuzzy reasoning and neural networks), learning/adaptation ability and human knowledge can be incorporated into a robot controller. In this paper, we propose a two-stage adaptive robot manipulator position/force control method in which the uncertain/unknown dynamic of the environment is compensated for in the task space and the joint friction is effectively compensated for in the joint space using soft computing techniques. The effectiveness of the proposed control method was evaluated by experiments.

Original languageEnglish
Pages (from-to)153-168
Number of pages16
JournalAdvanced Robotics
Volume14
Issue number3
DOIs
Publication statusPublished - 2000
Externally publishedYes

Fingerprint

Intelligent robots
Force control
Position control
Manipulators
Robots
Neural networks
Soft computing
Friction
Controllers
Genetic algorithms
Experiments

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

Cite this

Two-stage adaptive robot position/force control using fuzzy reasoning and neural networks. / Kiguchi, Kazuo; Watanabe, Keigo; Izumi, Kiyotaka; Fukuda, Toshio.

In: Advanced Robotics, Vol. 14, No. 3, 2000, p. 153-168.

Research output: Contribution to journalArticle

Kiguchi, Kazuo ; Watanabe, Keigo ; Izumi, Kiyotaka ; Fukuda, Toshio. / Two-stage adaptive robot position/force control using fuzzy reasoning and neural networks. In: Advanced Robotics. 2000 ; Vol. 14, No. 3. pp. 153-168.
@article{b7d1a0f3a5c24398b4e68ad7ca9aaa32,
title = "Two-stage adaptive robot position/force control using fuzzy reasoning and neural networks",
abstract = "Many studies have been performed on the position/force control of robot manipulators. Since the desired position and force required to realize certain tasks are usually designated in the operational space, the controller should adapt itself to an environment and generate the control force vector in the operational space. On the other hand, the friction of each joint of a robot manipulator is a serious problem since it impedes control accuracy. Therefore, the friction should be effectively compensated for in order to realize precise control of robot manipulators. Recently, soft computing techniques (fuzzy reasoning, neural networks and genetic algorithms) have been playing an important role in the control of robots. Applying the fuzzy-neuro approach (a combination of fuzzy reasoning and neural networks), learning/adaptation ability and human knowledge can be incorporated into a robot controller. In this paper, we propose a two-stage adaptive robot manipulator position/force control method in which the uncertain/unknown dynamic of the environment is compensated for in the task space and the joint friction is effectively compensated for in the joint space using soft computing techniques. The effectiveness of the proposed control method was evaluated by experiments.",
author = "Kazuo Kiguchi and Keigo Watanabe and Kiyotaka Izumi and Toshio Fukuda",
year = "2000",
doi = "10.1163/156855300741500",
language = "English",
volume = "14",
pages = "153--168",
journal = "Advanced Robotics",
issn = "0169-1864",
publisher = "Taylor and Francis Ltd.",
number = "3",

}

TY - JOUR

T1 - Two-stage adaptive robot position/force control using fuzzy reasoning and neural networks

AU - Kiguchi, Kazuo

AU - Watanabe, Keigo

AU - Izumi, Kiyotaka

AU - Fukuda, Toshio

PY - 2000

Y1 - 2000

N2 - Many studies have been performed on the position/force control of robot manipulators. Since the desired position and force required to realize certain tasks are usually designated in the operational space, the controller should adapt itself to an environment and generate the control force vector in the operational space. On the other hand, the friction of each joint of a robot manipulator is a serious problem since it impedes control accuracy. Therefore, the friction should be effectively compensated for in order to realize precise control of robot manipulators. Recently, soft computing techniques (fuzzy reasoning, neural networks and genetic algorithms) have been playing an important role in the control of robots. Applying the fuzzy-neuro approach (a combination of fuzzy reasoning and neural networks), learning/adaptation ability and human knowledge can be incorporated into a robot controller. In this paper, we propose a two-stage adaptive robot manipulator position/force control method in which the uncertain/unknown dynamic of the environment is compensated for in the task space and the joint friction is effectively compensated for in the joint space using soft computing techniques. The effectiveness of the proposed control method was evaluated by experiments.

AB - Many studies have been performed on the position/force control of robot manipulators. Since the desired position and force required to realize certain tasks are usually designated in the operational space, the controller should adapt itself to an environment and generate the control force vector in the operational space. On the other hand, the friction of each joint of a robot manipulator is a serious problem since it impedes control accuracy. Therefore, the friction should be effectively compensated for in order to realize precise control of robot manipulators. Recently, soft computing techniques (fuzzy reasoning, neural networks and genetic algorithms) have been playing an important role in the control of robots. Applying the fuzzy-neuro approach (a combination of fuzzy reasoning and neural networks), learning/adaptation ability and human knowledge can be incorporated into a robot controller. In this paper, we propose a two-stage adaptive robot manipulator position/force control method in which the uncertain/unknown dynamic of the environment is compensated for in the task space and the joint friction is effectively compensated for in the joint space using soft computing techniques. The effectiveness of the proposed control method was evaluated by experiments.

UR - http://www.scopus.com/inward/record.url?scp=0034499840&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0034499840&partnerID=8YFLogxK

U2 - 10.1163/156855300741500

DO - 10.1163/156855300741500

M3 - Article

VL - 14

SP - 153

EP - 168

JO - Advanced Robotics

JF - Advanced Robotics

SN - 0169-1864

IS - 3

ER -