Control of Efficient Intelligent Robotic Gripper Using Fuzzy Inference System

In the last few years the applications of artificial intelligence techniques have been used to convert human experience into a form understandable by computers. Advanced control based on artificial intelligence techniques is called intelligent control. Intelligent systems are usually described by analogies with biological systems by, for example, looking at how human beings perform control tasks, recognize patterns, or make decisions. Fuzzy logic is a way to make machines more intelligent enabling them to reason in a fuzzy manner like humans. Fuzzy logic, proposed by Lotfy Zadeh in 1965, emerged as a tool to deal with uncertain, imprecise, or qualitative decision-making problems (Zadeh, 1965).

[1]  Dan-El Neil Vila-Rosado,et al.  Hierarchical fuzzy control to ensure stable grasping , 2006, 2006 Seventh Mexican International Conference on Computer Science.

[2]  Michael A. Saliba,et al.  The mechanical and control system design of a dexterous robotic gripper , 2001, ICECS 2001. 8th IEEE International Conference on Electronics, Circuits and Systems (Cat. No.01EX483).

[3]  Vijay Kumar,et al.  Robotic grasping and contact: a review , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[4]  Sukran Yalpir,et al.  Adaptive network fuzzy inference system modeling for the input selection and prediction of anaerobic digestion effluent quality , 2011 .

[5]  Seema Chopra,et al.  Analysis of Fuzzy PI and PD Type Controllers Using Subtractive Clustering , 2006 .

[6]  R.D. Lorenz,et al.  A novel, compliant, four degree-of-freedom, robotic fingertip sensor , 1988, Conference Record of the 1988 IEEE Industry Applications Society Annual Meeting.

[7]  Thomas A. Runkler,et al.  Some issues in system identification using clustering , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[8]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[9]  Tharwat O. S. Hanafy A modified Algorithm to Model Highly Nonlinear System , 2010 .

[10]  Sungchul Kang,et al.  Development of tactile sensor for detecting contact force and slip , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Stephen L. Chin An Efficient Method for Extracting Fuzzy Classification Rules from High Dimensional Data , 1997, J. Adv. Comput. Intell. Intell. Informatics.

[12]  Venketesh N. Dubey,et al.  Grasping and Control Issues in Adaptive End Effectors , 2004 .

[13]  Vijay Kumar,et al.  Reduction of Fuzzy Rules and Membership Functions and Its Application to Fuzzy PI and PD Type Controllers , 2006 .

[14]  Ren C. Luo,et al.  An implementation of gripper control using the new slipping detector by multisensor fusion method , 2000, 2000 26th Annual Conference of the IEEE Industrial Electronics Society. IECON 2000. 2000 IEEE International Conference on Industrial Electronics, Control and Instrumentation. 21st Century Technologies.

[15]  Ayyoub Rezaeeian,et al.  ANFIS modeling and feed forward control of shape memory alloy actuators , 2008 .

[16]  M. F. Barsky,et al.  Robot gripper control system using PVDF piezoelectric sensors , 1989, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[17]  Danilo Emilio De Rossi,et al.  Tactile sensors and the gripping challenge , 1985 .

[18]  Patrick P. K. Lim,et al.  Sensory gripping system for variable products , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[19]  Dimitar Filev,et al.  Generation of Fuzzy Rules by Mountain Clustering , 1994, J. Intell. Fuzzy Syst..

[20]  Marco Ceccarelli,et al.  Grasp force control in two-finger grippers with pneumatic actuation , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[21]  Marcelo Godoy Simões,et al.  Fuzzy optimisation based control of a solar array system , 1999 .

[22]  Chin-Teng Lin,et al.  Efficient Self-Evolving Evolutionary Learning for Neurofuzzy Inference Systems , 2008, IEEE Transactions on Fuzzy Systems.

[23]  M.-J.E. Salami,et al.  Design of intelligent multifinger gripper for a robotic arm using a DSP-based fuzzy controller , 2000, 2000 TENCON Proceedings. Intelligent Systems and Technologies for the New Millennium (Cat. No.00CH37119).

[24]  Stephen Yurkovich,et al.  Fuzzy Control , 1997 .

[25]  Abdelhafid Zeghbib,et al.  ANFIS based modelling and control of non-linear systems : a tutorial , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).