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ANFIS Inverse Kinematics and Hybrid Control of a Human Leg Gait Model

Year 2013, Volume: 1 Issue: 2, 34 - 49, 01.08.2013
https://doi.org/10.5505/apjes.2013.24633

Abstract

A hybrid learning procedure referred to as adaptive neuro fuzzy inference system (ANFIS) is applied to an artificial

leg model to generate the correct positions of the servomotors actuating the leg joints. One of the most important

control problems of mechanical arms and legs is the efficient calculation of correct joint angles for a space

trajectory. Although this application represents the simplest model with two degrees of freedom, the practicality of

ANFIS for such mechanical systems is validated. For the gait model of the proposed mechanism, the experimental

planar motion of the ankle joint is transformed to joint angles by ANFIS and approximated by polynomial functions.

The corresponding servomotor positions are obtained by the proposed inverse kinematic solution method and are

included in a Simulink model as an embedded Matlab function. A hybrid control system consisting of combination

of a proportional plus derivative (PD) controller and a fuzzy logic controller (FLC) is applied to control the selected

servomotors. The accuracy of the control system is further verified on SimMechanics. 

References

  • [1] Zlajpah L. Simulation in Robotics. Mathematics and Computers in Simulation. 79879-897, 2008.
  • [2] Jazar RN. Theory of Applied Robotics. Springer;2006.
  • [3] Spong MW. Seth Hutchinson, M. Vidyasagar. Robot Modelling and Control. John Wiley and Sons; 2006.
  • [4] Qiao S, Liao Q, Wei S, Su HJ. Inverse kinematic analysis of the general 6R serial manipulators, Mechanism and Machine Theory 2010; 45:193–199.
  • [5] Manocha D, Canny JF. Efficient Inverse Kinematics for General 6R Manipulators, IEEE Transactions on Robotics and Automation 1994;Vol. 10, No. 5, 1994.
  • [6] Williams RL. II, Inverse Kinematics and Singularities of Manipulators with offset Wrist. IASTED International Journal of Robotics and Automation 1999;14-1:1-8.
  • [7] Howard DW and Zilouchian A. Application of Fuzzy Logic for the Solution of Inverse Kinematics and Hierarchical Controls of Robotic Manipulators. Journal of Intelligent and Robotic Systems 1998; 23:217–247.
  • [8] Jang JR. ANFIS: Adaptive-Network-Based Fuzzy Inference System. Man and Cybernetics 1993; 23- 3:665-685.
  • [9] Er MJ, Low CB, Nah KH, Lim MH, Yong NS. Real- time implementation of a dynamic fuzzy neural networks controller for a SCARA. Microprocessors and Microsystems 2002; 26:449-461.
  • [10] Popescu MC, Borcosi I, Olaru O, Popescu L and Grofu F. The simulation hybrid fuzzy control of a scara robot. WSEAS Transactions on Systems and Control 2008; 3:105-114.
  • [11] Doke J, Donelan MJ and Kuo AD. Mechanics and energetics of swinging the human leg.The Journal of Experimental Biology 2005;208:439-445.
  • [12] Arıtan S, Cilli M, Amca AM. HUBAG: 3 Dimensional motion analysis software. Sport Science Journal (in Turkish) 2010;211:30 36. ISSN : 1300 3119
  • [13] Siciliano B, Khatip O. Handbook of Robotics. Springer, 2008. [14] Ogata K. Modern Control Engineering. Prentice-Hall, Inc., 1997.
  • 15] Yang Y, Zhou C. Robust Adaptive Fuzzy Control for Permanent Magnet Synchronous Servomotor Drives. International Journal of Intelligent Systems 2005; 20:153-171.
  • [16] Shieh MY and Li TS. Design and implementation of integrated fuzzy logic controller for a servomotor system. Mechatronics 1998;8:217-240.
  • [17] Çunkas M and Aydoğdu O. Realization of fuzzy Logic controlled brushless dc motor drives using Matlab/Simulink. Mathematical and Computational Applications 2010;15- 2:218-229.
  • [18] Ankarali A. ANFIS inverse kinematics and precise trajectory tracking of a dual arm robot. Proceedings of the 2012 International Conference on Modelling, Simulation and Visualization Methods. 270-274, WORLDCOMP'12, July16-19, Las Vegas Nevada, USA, 2012
  • [19] Suzuki K. Artificial Neural NetworksMethodological Advances and Biomedical Applications. InTech 2011.
  • [20] Passino KM, Yurkovich S. Fuzzy Control. Addison-Wesley Longman;1998.
  • [21] Genc HM, Yesil E, Eksin I, Guzelkaya M, Tekin OA. A rule base modification scheme in fuzzy controllers for time-delay systems. Expert Systems with Applications 2009;36:8476-8486.

ANFIS Inverse Kinematics and Hybrid Control of a Human Leg Gait Model

Year 2013, Volume: 1 Issue: 2, 34 - 49, 01.08.2013
https://doi.org/10.5505/apjes.2013.24633

Abstract

A hybrid learning procedure referred to as adaptive neuro fuzzy inference system (ANFIS) is applied to an artificial leg model to generate the correct positions of the servomotors actuating the leg joints. One of the most important control problems of mechanical arms and legs is the efficient calculation of correct joint angles for a space trajectory. Although this application represents the simplest model with two degrees of freedom, the practicality of ANFIS for such mechanical systems is validated. For the gait model of the proposed mechanism, the experimental planar motion of the ankle joint is transformed to joint angles by ANFIS and approximated by polynomial functions. The corresponding servomotor positions are obtained by the proposed inverse kinematic solution method and are included in a Simulink model as an embedded Matlab function. A hybrid control system consisting of combination of a proportional plus derivative (PD) controller and a fuzzy logic controller (FLC) is applied to control the selected servomotors. The accuracy of the control system is further verified on SimMechanics.

References

  • [1] Zlajpah L. Simulation in Robotics. Mathematics and Computers in Simulation. 79879-897, 2008.
  • [2] Jazar RN. Theory of Applied Robotics. Springer;2006.
  • [3] Spong MW. Seth Hutchinson, M. Vidyasagar. Robot Modelling and Control. John Wiley and Sons; 2006.
  • [4] Qiao S, Liao Q, Wei S, Su HJ. Inverse kinematic analysis of the general 6R serial manipulators, Mechanism and Machine Theory 2010; 45:193–199.
  • [5] Manocha D, Canny JF. Efficient Inverse Kinematics for General 6R Manipulators, IEEE Transactions on Robotics and Automation 1994;Vol. 10, No. 5, 1994.
  • [6] Williams RL. II, Inverse Kinematics and Singularities of Manipulators with offset Wrist. IASTED International Journal of Robotics and Automation 1999;14-1:1-8.
  • [7] Howard DW and Zilouchian A. Application of Fuzzy Logic for the Solution of Inverse Kinematics and Hierarchical Controls of Robotic Manipulators. Journal of Intelligent and Robotic Systems 1998; 23:217–247.
  • [8] Jang JR. ANFIS: Adaptive-Network-Based Fuzzy Inference System. Man and Cybernetics 1993; 23- 3:665-685.
  • [9] Er MJ, Low CB, Nah KH, Lim MH, Yong NS. Real- time implementation of a dynamic fuzzy neural networks controller for a SCARA. Microprocessors and Microsystems 2002; 26:449-461.
  • [10] Popescu MC, Borcosi I, Olaru O, Popescu L and Grofu F. The simulation hybrid fuzzy control of a scara robot. WSEAS Transactions on Systems and Control 2008; 3:105-114.
  • [11] Doke J, Donelan MJ and Kuo AD. Mechanics and energetics of swinging the human leg.The Journal of Experimental Biology 2005;208:439-445.
  • [12] Arıtan S, Cilli M, Amca AM. HUBAG: 3 Dimensional motion analysis software. Sport Science Journal (in Turkish) 2010;211:30 36. ISSN : 1300 3119
  • [13] Siciliano B, Khatip O. Handbook of Robotics. Springer, 2008. [14] Ogata K. Modern Control Engineering. Prentice-Hall, Inc., 1997.
  • 15] Yang Y, Zhou C. Robust Adaptive Fuzzy Control for Permanent Magnet Synchronous Servomotor Drives. International Journal of Intelligent Systems 2005; 20:153-171.
  • [16] Shieh MY and Li TS. Design and implementation of integrated fuzzy logic controller for a servomotor system. Mechatronics 1998;8:217-240.
  • [17] Çunkas M and Aydoğdu O. Realization of fuzzy Logic controlled brushless dc motor drives using Matlab/Simulink. Mathematical and Computational Applications 2010;15- 2:218-229.
  • [18] Ankarali A. ANFIS inverse kinematics and precise trajectory tracking of a dual arm robot. Proceedings of the 2012 International Conference on Modelling, Simulation and Visualization Methods. 270-274, WORLDCOMP'12, July16-19, Las Vegas Nevada, USA, 2012
  • [19] Suzuki K. Artificial Neural NetworksMethodological Advances and Biomedical Applications. InTech 2011.
  • [20] Passino KM, Yurkovich S. Fuzzy Control. Addison-Wesley Longman;1998.
  • [21] Genc HM, Yesil E, Eksin I, Guzelkaya M, Tekin OA. A rule base modification scheme in fuzzy controllers for time-delay systems. Expert Systems with Applications 2009;36:8476-8486.
There are 20 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Arif Ankarali This is me

Murat Cilli This is me

Publication Date August 1, 2013
Submission Date November 14, 2015
Published in Issue Year 2013 Volume: 1 Issue: 2

Cite

IEEE A. Ankarali and M. Cilli, “ANFIS Inverse Kinematics and Hybrid Control of a Human Leg Gait Model”, APJES, vol. 1, no. 2, pp. 34–49, 2013, doi: 10.5505/apjes.2013.24633.