Real Time Control Application of the Robotic Arm Using Neural Network Based Inverse Kinematics Solution

Year 2021, Volume: 25 Issue: 3, 849 - 857, 30.06.2021

Nurettin Gökhan Adar

https://doi.org/10.16984/saufenbilder.907312

Cited By: 4

Abstract

Robotic arms are widely used in many industrial applications at present. The control of robotic arms involves position coordination Cartesian space by a forward/inverse kinematics solution method. The inverse kinematics is difficult for real-time control applications, computational requirements are intensive and the run-time is high. The traditional solution methods used geometric, algebraic, and numerical iterative techniques are inadequate and slow in the inverse kinematics solution. Recently, alternative solution methods based on artificial intelligence techniques have been developed to solve the inverse kinematics problem. In this study, a multi-layered feed-forward Artificial Neural Network model was developed to solve the inverse kinematics of the 5 degrees of freedom robotic arm. Using the Proportional-Integral control algorithm combined with this Artificial Neural Network model, the real-time position control of the robotic arm was accomplished. The obtained results were compared with the PI control supported by an analytical inverse kinematics solution in real-time. The results showed that the PI control combined with Artificial Neural Network has superior tracking ability, smaller control error, and better absolute fit to the reference.

Keywords

Robotic arm, Artificial neural network, Inverse kinematics, PI control

References

• [1] C. Song, S. Xie, Z. Zhou, and Y. Hu, “Modeling of pneumatic artificial muscle using a hybrid artificial neural network approach. Mechatronics,” vol. 31, pp. 124-131, 2015.
• [2] F. Luan, J. Na, Y. Huang, and G. Gao, “Adaptive neural network control for robotic manipulators with guaranteed finite-time convergence,” Neurocomputing, vol. 337, pp. 153-164, 2019.
• [3] K. Zheng, Q. Zhang, Y. Hu and B. Wu, “Design of fuzzy system-fuzzy neural network-backstepping control for complex robot system,” Information Sciences, vol. 546, pp. 1230-1255, 2021.
• [4] Y. Wang, Y. Shi, D. Ding and X. Gu, “Double global optimum genetic algorithm–particle swarm optimization-based welding robot path planning,” Engineering Optimization, vol. 48, pp. 299-316, 2016
• [5] S. Dereli and R. Köker, “IW-PSO Approach to the Inverse Kinematics Problem Solution of a 7-DOF Serial Robot Manipulator,” Sigma Journal of Engineering and Natural Sciences, vol. 36, pp. 77-85, 2018
• [6] A.K. Sadhu, A. Konar, T. Bhattacharjee and S. Das, “Synergism of Firefly Algorithm and Q-Learning for Robot Arm Path Planning,” Swarm and Evolutionary Computation, vol. 43, pp. 50-68, 2018
• [7] R. Köker, “A genetic algorithm approach to a neural-network-based inverse kinematics solution of robotic manipulators based on error minimization,” Information Sciences, vol. 222, pp. 528-543, 2013.
• [8] B. Karlik and S. Aydin, “An improved approach to the solution of inverse kinematics problems for robot manipuşators,” Engineering Applications of Artificial Intelligence, vol. 13, pp. 159-164, 2000.
• [9] R. Köker, C. Öz, T. Çakar and H. Ekiz, “A study of neural network based inverse kinematics solution for a three-joint robot,” Robotics and Autonomous Systems, vol. 49, pp. 227-234, 2004.
• [10] R.V. Mayorga and P. Sanongboon, “Inverse kinematics and geometrically bounded singularities prevention of redundant manipulators: An artificial neural network approach,” Robotics and Autonomous Systems, vol. 53, pp. 164-176, 2005.
• [11] B. Daya, S. Khawandi and M. Akoum, “Applying Neural Network Architecture for Inverse Kinematics Problem in Robotics,” J. Software Engineering & Applications, vol. 3, pp. 230-239, 2010.
• [12] A.V. Duka, “Neural network based inverse kinematics solution for trajectory tracking of a robotic arm,” Procedia Technology, vol. 12, pp. 20-27, 2014.
• [13] Zacharie M., “Advanced Logistic Belief Neural Network Algorithm for Robot Arm Control”, Journal of Computer Science, vol. 8, no. 6, pp. 965-970, 2012.
• [14] A. El-Sherbiny, M.A. Elhosseini and A.Y. Haikal, “A comparative study of soft computing methods to solve inverse kinematics problem,” Ain Shams Engineering Journal, vol. 9, no. 4, pp. 2535-2548, 2018.
• [15] Z.H. Jiang and T. Ishita, “A Neural Network Controller for Trajectory Control of Industrial Robot Manipulators,” vol. 3, no. 8, pp. 1-8, August 2008.
• [16] Z. Xu, S. Li, X. Zhou, W. Yan, T. Cheng and D. Huang, “Dynamic neural networks based kinematic control for redundant manipulators with model uncertainties,” vol. 329, pp. 255-266, February, 2019.
• [17] R. Sharma, P. Gaur and A.P. Mittal, “Performance analysis of two-degree of freedom fractional order PID controllers for robotic manipulator with payload,” ISA Transactions, vol. 58, pp. 279-91, 2015.
• [18] N. G. Adar, “Desing, Manufacturing and Control of Mobile Robot” Ph.D. dissertation, Dept. Mech. Eng., Sakarya Univ., Sakarya, 2016.