Prediction of contact forces of underactuated finger by adaptive neuro fuzzy approach

被引：15

作者：

Petkovic, Dalibor ^{[1
]}

Shamshirband, Shahaboddin ^{[2
]}

Abbasi, Almas ^{[3
]}

Kiani, Kourosh ^{[4
]}

Al-Shammari, Eiman Tamah ^{[5
]}

机构：

[1] Univ Nis, Fac Mech Engn, Deparment Mechatron & Control, Nish 18000, Serbia

[2] Univ Malaya, Fac Comp Sci & Informat Technol, Dept Comp Syst & Informat Technol, Kuala Lumpur 50603, Malaysia

[3] Univ Malaya, Fac Comp Sci & Informat Technol, Dept Artificial Intelligence, Kuala Lumpur 50603, Malaysia

[4] Semnan Univ, Fac Elect & Comp Engn, Semnan, Iran

[5] Kuwait Univ, Coll Comp Sci & Engn, Dept Informat Sci, Kuwait, Kuwait

来源：

MECHANICAL SYSTEMS AND SIGNAL PROCESSING | 2015年 / 64-65卷

关键词：

Underactuated finger; Neuro fuzzy; Prediction; Kinetostatic analysis; Contact forces; INFERENCE SYSTEM; SCREW THEORY; ROBOTIC GRIPPER; ANFIS; DESIGN; PLANAR; ALLOY;

D O I：

10.1016/j.ymssp.2015.03.013

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

To obtain adaptive finger passive underactuation can be used. Underactuation principle can be used to adapt shapes of the fingers for grasping objects. The fingers with underactuation do not require control algorithm. In this study a kinetostatic model of the underactuated finger mechanism was analyzed. The underactuation is achieved by adding the compliance in every finger joint. Since the contact forces of the finger depend on contact position of the finger and object, it is suitable to make a prediction model for the contact forces in function of contact positions of the finger and grasping objects. In this study prediction of the contact forces was established by a soft computing approach. Adaptive neuro-fuzzy inference system (ANFIS) was applied as the soft computing method to perform the prediction of the finger contact forces. (C) 2015 Elsevier Ltd. All rights reserved.

引用

页码：520 / 527

页数：8

共 27 条

[1]

Al-Ghandoor A, 2009, JORDAN J MECH IND EN, V3, P69

[2] Mobility analysis of generalized angulated scissor-like elements with the reciprocal screw theory [J].

Cai, Jianguo ;

Deng, Xiaowei ;

Feng, Jian ;

Xu, Yixiang .

MECHANISM AND MACHINE THEORY, 2014, 82 :256-265

[3]

Domínguez-López JA, 2004, ROBOT AUTON SYST, V48, P93, DOI 10.1016/j.robot.2004.06.001

[4] Prediction of building energy needs in early stage of design by using ANFIS [J].

Ekici, Betul Bektas ;

Aksoy, U. Teoman .

EXPERT SYSTEMS WITH APPLICATIONS, 2011, 38 (05) :5352-5358

[5] An ANFIS model for the prediction of flow stress of Ti600 alloy during hot deformation process [J].

Han, Yuanfei ;

Zeng, Weidong ;

Zhao, Yongqing ;

Qi, YunLian ;

Sun, Yu .

COMPUTATIONAL MATERIALS SCIENCE, 2011, 50 (07) :2273-2279

[6] Design of flexure-based precision transmission mechanisms using screw theory [J].

Hopkins, Jonathan B. ;

Panas, Robert M. .

PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR PRECISION ENGINEERING AND NANOTECHNOLOGY, 2013, 37 (02) :299-307

[7] ANFIS - ADAPTIVE-NETWORK-BASED FUZZY INFERENCE SYSTEM [J].

JANG, JSR .

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, 1993, 23 (03) :665-685

[8] Predicting optimum cure time of rubber compounds by means of ANFIS [J].

Karaagac, Bagdagul ;

Inal, Melih ;

Deniz, Veli .

MATERIALS & DESIGN, 2012, 35 :833-838

[9] Application of adaptive neuro-fuzzy inference system for solubility prediction of carbon dioxide in polymers [J].

Khajeh, Aboozar ;

Modarress, Hamid ;

Rezaee, Babak .

EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36 (03) :5728-5732

[10] Prediction of the effect of vacuum sintering conditions on porosity and hardness of porous NiTi shape memory alloy using ANFIS [J].

Khalifehzadeh, Razieh ;

Forouzan, Saeed ;

Arami, Hamed ;

Sadrnezhaad, S. K. .

COMPUTATIONAL MATERIALS SCIENCE, 2007, 40 (03) :359-365

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