i-manager Publications

Tracking Path Control Of Robotic Manipulators Using Radial Basis Function Neural Network

sheilza jain*, Anika Chhabra**

* Assistant Professor, Electronics Engineering Department, YMCA University of Science and Technology, Faridabad, India.

** M.Tech Student, Electronics Engineering Department, YMCA University of Science and Technology, Faridabad, India

Periodicity:May - July'2014
DOI : https://doi.org/10.26634/jic.2.3.3057

Abstract

Robotic manipulators have been extensively used in the industrial applications such as paint spraying, welding, accurate positioning system etc. where joint angles of robotic manipulators are directed to follow some given trajectories as close as possible. Therefore, trajectory tracking problem of robotic manipulators is the most significant and fundamental task for researchers to work upon. Robotic manipulator systems are inevitably subject to structured and unstructured uncertainties resulting in imprecision of its dynamical models and it is difficult to obtain a suitable mathematical model for the robotic control scheme. Robotic manipulators are dynamically coupled, multi-inputmulti- output, non-linear and time variant complex systems. This paper presents the dynamics of two link robotic manipulator. In this paper, PID (Proportional Integral Derivative), CTC (Computed Torque Control), SMC (Sliding Mode Control) and RBFNN (Radial Basis Function Neural Network) controllers are designed and implemented to the joint position control of two link robotic manipulators for pre-defined trajectory tracking control. Simulated results for different controllers are compared to show reduction in tracking error and performance improvement of two link robotic manipulators. Tracking performance and error comparison graphs are presented to demonstrates the performance of the proposed controllers. Further, comparison between chattering of SMC and RBFNN-SMC for joint 1 and joint 2 is also shown. The simulation work is carried out in MATLAB environment.

Keywords

Robotic manipulator, Tracking control, PID, Computed Torque Control, SMC and Radial Basis Function Neural Network.

How to Cite this Article?

Jain, S., and Chhabra, A. (2014). Tracking Path Control of Robotic Manipulators using Radial Basis Function Neural Network. i-manager’s Journal on Instrumentation and Control Engineering, 2(3), 11-18. https://doi.org/10.26634/jic.2.3.3057

References

[1]. H. Seraji, (1986). “Linear Multivariable Control of Robot Manipulators”, Proceedings of IEEE International Conference on Robotics and Automation, pp. 565-571.

[2]. L. Chun-Liang, J. Horn-Yong, and S. Niahn-Chung, (2003)."GA-based multi objective PID control for a linear brushless DC motor," IEEE/ASME Transactions on Mechatronics, Vol. 8, pp. 56-65.

[3]. N. T. Duy, M. Seegar and J. Peters, (2008). “Computed Torque Control with Nonparametric Regression Models”, Proceeding of IEEE Conference on Control, pp. 212-217.

[4]. Daoxiang Gao; Dingyu Xue (2006). "Terminal Sliding Mode Adaptive Control for Robotic Manipulators," Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on , Vol.2, No., pp.8853,8857

[5]. Man Zhihong, Yu, X.H., Eshraghian, K., Palaniswami, M., (1995). "A robust adaptive sliding mode tracking control using an RBF neural network for robotic manipulators,", 1995. Proceedings IEEE International Conference on Neural Networks, Vol.5, No., pp.2403-2408.

[6]. H. Pai-Yi and C. Yung-Yaw, (1997). "Design of PID controller for precision positioning Table using genetic th algorithm” Proceedings of the 36 IEEE Conference on Decision and Control”, Vol.3, pp. 2513-2514.

[7]. Sheilza Jain, Anika Chabra, (2014). “Control for robotic manipulators with proportional integral derivative & computed torque control,” International Journal of Advances in Engineering Sciences, Vol.4, Issue 2, pp. 8-11.

[8]. Lu Hung-Ching, Cheng-Hung Psai and Ming-Hung Chang, (2010). “Radial Basis Function Neural Network with Sliding Mode Control for Robotic Manipulators”, IEEE conference on neural network, pp.1209-1215.

[9]. M. W. Spong, M. Vidyasagar, “Robot Dynamics and Control”, Wiley-India Edition, NewYork.

[10]. Morris A.S. and Madani A. (1996). Computed Torque vs. Quadratic Optimal Control for a Two-Flexible-Link Robot, Proceeding of IEEE International Conference on Control, pp. 335-340.

[11]. A. Mohammad and S. S. Ehsan, (2008). "Sliding mode PID controller design for robot manipulators by using fuzzy tuning approach", IEEE Chinese Control Conference, pp. 170-175.

[12]. Lewis, F.L., Yesildirek, A., Kai Liu, (1996). "Multilayer neural-net robot controller with guaranteed tracking performance," Neural Networks, IEEE Transactions on control, Vol.7, No.2, pp.388-399.

[13]. N. Suda, (1992). PID Control, Asakura Shoten.

[14]. M. Uebel, I. Minis and K. Cleary, (1992). “ Improved Computed Torque Control for Industrial Robots”, Proceeding of International Conference on Robotics and Automation, Vol. 1, pp. 528-533

[15]. Man Zhihong, Paplinski, A.P., Wu, H.R., (1994). "A robust MIMO terminal sliding mode control scheme for rigid robotic manipulators," IEEE Transactions on Automatic Control, Vol.39, No.12, pp.2464-2469.

[16]. Lewis, F.L., Liu, K., Yesildirek, A. (1995). "Neural net robot controller with guaranteed tracking performance", IEEE Transactions on Neural Networks, Vol.6, No.3, pp.703- 715.

[17]. K. J. Astrom and T. Hagglund, (2001). "The future of PID control," Control Engineering Practice, Vol. 9, pp. 1163- 1175.

[18]. Riko Safaric and Karel Jezernik, (1994). “Trajectory Tracking Neural Network Controller for a Robot Mechanism And Laypunov Theory of Stability”, Proceeding of IEEE/RSJ International Conference on Intelligent Robots and Systems, Vol. 1, pp. 626-633.

Tracking Path Control Of Robotic Manipulators Using Radial Basis Function Neural Network

Abstract

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