Year 2018,
Volume: 2 Issue: 1, 27 - 32, 15.04.2018
Tolgay Kara
,
Ali Hussien Mary
References
- 1. Alavandar, S., and M. J. Nigam, Inverse kinematics solution of 3DOF planar robot using ANFIS. Int. J. of Computers, Communications & Control, 2008. 3: p. 150-155.
- 2. Shen, W, J. Gu, and E. E. Milios, Self-configuration fuzzy system for inverse kinematics of robot manipulators, in IEEE Annual meeting of the North American Fuzzy Information Processing Society NAFIPS 2006: p. 41-45.
- 3. Tarokh, Mahmoud, and M. Kim, Inverse kinematics of 7-DOF robots and limbs by decomposition and approximation. IEEE transactions on robotics, 2007. 23(3): p. 595-600.
- 4. Duka, A. V., ANFIS based Solution to the Inverse Kinematics of a 3 DOF planar Manipulator. Procedia Technology, 2015. 19: p. 526-533.
- 5. Duka, A. V., Neural network based inverse kinematics solution for trajectory tracking of a robotic arm. Procedia Technology, 2014. 12: p. 20-27.
- 6. Ma, C., Z. Yong, C. Jin., W. Bin, and Z. Qinjun, Inverse kinematics solution for 6R serial manipulator based on RBF neural network. In International Conference on Advanced Mechatronic Systems ICAMechS 2016: p. 350-355.
- 7. Mayorga, R. V., and P. Sanongboon, Inverse kinematics and geometrically bounded singularities prevention of redundant manipulators: An Artificial Neural Network approach. Robotics and Autonomous Systems, 2005. 53(3): p. 164-176.
- 8. Pérez-Rodríguez, R, M-C. Alexis, C. Ursula, S. Javier, C. Cesar, O. Eloy, M. T. Josep, M. Josep, and J. G. Enrique, Inverse kinematics of a 6 DoF human upper limb using ANFIS and ANN for anticipatory actuation in ADL-based physical Neurorehabilitation. Expert Systems with Applications, 2012. 39(10): p. 9612-9622.
- 9. Zou, X., G. Dawei, W. Liping, and G. Zhenyu, A novel method to solve inverse variational inequality problems based on neural networks, Neurocomputing, 2016. 173(3): p. 1163-1168.
- 10. Assal, S. F. M, W. Keigo, and I. Kiyotaka, Neural Network-Based Kinematic Inversion of Industrial Redundant Robots Using Cooperative Fuzzy Hint for the Joint Limits Avoidance. IEEE/ASME Transactions on mechatronics, 2016. 11(5): p. 593-603.
- 11. Lazarevska, E., A Neuro-Fuzzy Model of the Inverse Kinematics of a 4 DOF Robotic Arm, in IEEE 14th International Conference on Modelling and Simulation UKSim2012: p. 306-311.
- 12. Köker, R., Reliability-based approach to the inverse kinematics solution of robots using Elman's networks. Engineering applications of artificial intelligence, 2005. 18(6): p. 685-693.
- 13. Rolf, M. and J. J. Steil, Efficient Exploratory Learning of Inverse Kinematics on a Bionic Elephant Trunk. Neural Networks and Learning Systems, IEEE Transactions on, 2013. 25(6): p. 1147-1160.
- 14. Slotine, J-J E., and W. Li, Applied nonlinear control. Vol. 199. No. 1. Englewood Cliffs, NJ: Prentice hall, 1991.
- 15. Fallaha, C. J., M. Saad, H. Y. Kanaan, and K. Al-Haddad, Sliding-mode robot control with exponential reaching law. IEEE Transactions on Industrial Electronics, 2001, 58(2), 600-610.
- 16. Kachroo, P. and M. Tomizuka, Chattering reduction and error convergence in the sliding-mode control of a class of nonlinear systems. IEEE Transactions on Automatic Control , 1996, 41(7): p.1063-1068.
- 17. Eker, I., Sliding mode control with PID sliding surface and experimental application to an electromechanical plant. ISA transactions, 2006, 45(1): p.109-118.
- 18. Parra-Vega, V., S. Arimoto, Y. H. Liu, G. Hirzinger, and P. Akella,(2003). Dynamic sliding PID control for tracking of robot manipulators: Theory and experiments. IEEE Transactions on Robotics and Automation, 2003, 19(6): p. 967-976.
Feedback-based IKP solution with SMC for robotic manipulators: the SCARA example
Year 2018,
Volume: 2 Issue: 1, 27 - 32, 15.04.2018
Tolgay Kara
,
Ali Hussien Mary
Abstract
This paper presents a novel
scheme for solving inverse kinematics problem (IKP) of a multi-link robotic
manipulator. Important features of the proposed strategy are generality and
simplicity regardless of the number of degrees of freedom (DOF) and geometry of
the robot. The proposed method is a feedback strategy where the IKP solution is
expressed as a dynamic control system whose goal is to maintain satisfactory
trajectory tracking. As a simulation test to reveal the performance of proposed
scheme, a four DOF Selective Compliance Assembly Robot Arm (SCARA) system is
considered. Feedback law in proposed closed-loop solution method is selected as
a combination of Sliding Mode Control (SMC) and Proportional-Derivative (PD) control
for providing simplicity and robustness. Simulation results are used to show
the efficacy of proposed IKP solution approach in comparison with commonly used
neural networks (NN) based IKP solution method. Results reveal that proposed
method yields the solution of IKP with satisfactory performance.
References
- 1. Alavandar, S., and M. J. Nigam, Inverse kinematics solution of 3DOF planar robot using ANFIS. Int. J. of Computers, Communications & Control, 2008. 3: p. 150-155.
- 2. Shen, W, J. Gu, and E. E. Milios, Self-configuration fuzzy system for inverse kinematics of robot manipulators, in IEEE Annual meeting of the North American Fuzzy Information Processing Society NAFIPS 2006: p. 41-45.
- 3. Tarokh, Mahmoud, and M. Kim, Inverse kinematics of 7-DOF robots and limbs by decomposition and approximation. IEEE transactions on robotics, 2007. 23(3): p. 595-600.
- 4. Duka, A. V., ANFIS based Solution to the Inverse Kinematics of a 3 DOF planar Manipulator. Procedia Technology, 2015. 19: p. 526-533.
- 5. Duka, A. V., Neural network based inverse kinematics solution for trajectory tracking of a robotic arm. Procedia Technology, 2014. 12: p. 20-27.
- 6. Ma, C., Z. Yong, C. Jin., W. Bin, and Z. Qinjun, Inverse kinematics solution for 6R serial manipulator based on RBF neural network. In International Conference on Advanced Mechatronic Systems ICAMechS 2016: p. 350-355.
- 7. Mayorga, R. V., and P. Sanongboon, Inverse kinematics and geometrically bounded singularities prevention of redundant manipulators: An Artificial Neural Network approach. Robotics and Autonomous Systems, 2005. 53(3): p. 164-176.
- 8. Pérez-Rodríguez, R, M-C. Alexis, C. Ursula, S. Javier, C. Cesar, O. Eloy, M. T. Josep, M. Josep, and J. G. Enrique, Inverse kinematics of a 6 DoF human upper limb using ANFIS and ANN for anticipatory actuation in ADL-based physical Neurorehabilitation. Expert Systems with Applications, 2012. 39(10): p. 9612-9622.
- 9. Zou, X., G. Dawei, W. Liping, and G. Zhenyu, A novel method to solve inverse variational inequality problems based on neural networks, Neurocomputing, 2016. 173(3): p. 1163-1168.
- 10. Assal, S. F. M, W. Keigo, and I. Kiyotaka, Neural Network-Based Kinematic Inversion of Industrial Redundant Robots Using Cooperative Fuzzy Hint for the Joint Limits Avoidance. IEEE/ASME Transactions on mechatronics, 2016. 11(5): p. 593-603.
- 11. Lazarevska, E., A Neuro-Fuzzy Model of the Inverse Kinematics of a 4 DOF Robotic Arm, in IEEE 14th International Conference on Modelling and Simulation UKSim2012: p. 306-311.
- 12. Köker, R., Reliability-based approach to the inverse kinematics solution of robots using Elman's networks. Engineering applications of artificial intelligence, 2005. 18(6): p. 685-693.
- 13. Rolf, M. and J. J. Steil, Efficient Exploratory Learning of Inverse Kinematics on a Bionic Elephant Trunk. Neural Networks and Learning Systems, IEEE Transactions on, 2013. 25(6): p. 1147-1160.
- 14. Slotine, J-J E., and W. Li, Applied nonlinear control. Vol. 199. No. 1. Englewood Cliffs, NJ: Prentice hall, 1991.
- 15. Fallaha, C. J., M. Saad, H. Y. Kanaan, and K. Al-Haddad, Sliding-mode robot control with exponential reaching law. IEEE Transactions on Industrial Electronics, 2001, 58(2), 600-610.
- 16. Kachroo, P. and M. Tomizuka, Chattering reduction and error convergence in the sliding-mode control of a class of nonlinear systems. IEEE Transactions on Automatic Control , 1996, 41(7): p.1063-1068.
- 17. Eker, I., Sliding mode control with PID sliding surface and experimental application to an electromechanical plant. ISA transactions, 2006, 45(1): p.109-118.
- 18. Parra-Vega, V., S. Arimoto, Y. H. Liu, G. Hirzinger, and P. Akella,(2003). Dynamic sliding PID control for tracking of robot manipulators: Theory and experiments. IEEE Transactions on Robotics and Automation, 2003, 19(6): p. 967-976.