DYNAMIC MODEL AND CONTROL OF 2-DOF ROBOTIC ARM

Yıl 2018, , 141 - 150, 29.12.2018

Mesut Hüseyinoğlu , Tayfun Abut

https://doi.org/10.36222/ejt.497852

Cited By: 6

Öz

Robotic is a relatively young field of
modern technology that exceeds traditional engineering boundaries. Control of
the robots is important due to the fact that it has a usage area in many areas.
In this study, modelling and control of two degrees of freedom (2-DOF) robotic
arm were carried out. Lagrange-Euler method was used to obtain the dynamic
equations of the robot. The system was controlled in the simulation
environment. Sliding-Mode Control (SMC) and Proportional-Integral-Derivative
(PID) control methods were proposed to control the 2 DOF robotic arm. The
saturation function is used for the chattering problem of the sliding mode
control method. Both process noise and measurement noise have been applied to
control the robot in conditions close to the actual ambient conditions. The
control methods applied according to the results of the simulation environment
were compared and the results were examined.

Anahtar Kelimeler

Sliding Mode Control, PID Control, Dynamic Model, , 2-DOF Robotic Arm

Kaynakça

• [1] Pan, L., Gao, T., Xu, F., Zhang, L., Enhanced Robust Motion Tracking Control for 6 Degree-of-freedom Industrial Assembly Robot with Disturbance Adaption, International Journal of Control, Automation and Systems, 16 (2018), 2, pp. 921-928
• [2] Meng, D., Moore, K. L., Robust Iterative Learning Control for Nonrepetitive Uncertain Systems, IEEE Transactions on Automatic Control, 62 (2017), 2, pp. 907-913
• [3] Chaudhary, H., Panwar, V., Prasad, R., Sukavanam, N., Adaptive Neuro Fuzzy Based Hybrid Force/Position Control for an Industrial Robot Manipulator, Journal of Intelligent Manufacturing, 27 (2016), 6, pp. 1299-1308
• [4] Lochan, K., Roy, B. K., Control of Two-link 2-DOF Robot Manipulator Using Fuzzy Logic Techniques: A Review, Proceedings, Fourth International Conference on Soft Computing for Problem Solving, Warsaw, Poland, 2014
• [5] Piltan, F., Nabaee, A., Ebrahimi, M., Bazregar, M., Design robust fuzzy sliding mode control technique for robot manipulator systems with modeling uncertainties, International Journal of Information Technology and Computer Science, 5 (2013), 123-135
• [6] Hsu, S. H., Fu, L. C., Adaptive decentralized control of robot manipulators driven by current-fed induction motors, IEEE/ASME Trans. Mechatronic, 10 (2005), 4, pp. 465-468
• [7] Yang, Z. J., Fukushima, Y., Qin, P., Decentralized adaptive robust control of robot manipulators using disturbance observers, IEEE Transactions on Control Systems Technology, 20 (2012), 5, pp. 1357-1365
• [8] Cronin, J., Escano, J. M., Roshany-Yamchi, S., Canty, N., Fuzzy-Based Generalized Predictive Control of a Robotic Arm, 25th IET Irish Signals & Systems Conference, 2014
• [9] Dou H. B., Wang ,S. P., Robust adaptive motion/force control formotion synchronization of multiple uncertain two-link manipulators, Mechanism and Machine Theory, 67 (2013), pp. 77-93
• [10] Yao, J. Y., Jiao, Z. X., Yao, B., Shang, Y. X., Dong, W. B., Nonlinear adaptive robust force control of hydraulic load simulator, Chinese Journal of Aeronautics, 25 (2012), pp.766-775
• [11] Wijesoma, S. W., Richards, R. J., Robust Trajectory Following of Robots Using Computed Torque Structures with VSS, International Journal of Control, 52 (1990), 4, pp. 935-962
• [12] Mendes, N., Neto, P., Indirect adaptive fuzzy control for industrial robots: a solution for contact applications, Expert Systems with Applications, 42 (2015), 22, pp. 929-935
• [13] He, W., Chen, Y.,Yin, Z., Adaptive neural network control of an uncertain robot with full-state constraints, IEEE Trans. Cybern, 46 (2016), 3, pp. 620-629
• [14] He, W., Dong, Y., Sun, C., Adaptive neural impedance control of a robotic manipulator with input saturation, IEEE Trans. Syst. Man Cybern.: Syst, 46 (2016), 3, pp. 334-344
• [15] Nikdel, N., Badamchizadeh, M. A., Azimirad, V., Nazari, M. A., Adaptive backstepping control for an n-degree of freedomrobotic manipulator based on combined state augmentation, Robotics and Computer-Integrated Manufacturing, 30 (2017), 44, pp. 129-143
• [16] Hazewinkel, M., Lagrange equations (in mechanics)-Encyclopedia of Mathematics, Springer, Netherlands, 1990
• [17] Abut, T., Modeling and Optimal Control of a DC Motor. Int. J. Eng. Trends Technol., 32 (2016), 3, pp. 146-150
• [18] Ziegler, G., Nichols, N. B., Optimum settings for automatic controllers, Journal of Dynamic Systems, Measurement, and Control, 115 (1993), 2B, pp. 220-222
• [19] Aström, K. J., Hägglund, T., PID controllers: theory, design, and tuning, Instrument Society of USA, 1995
• [20] Bailey, E., Arapostathis, A., Simple sliding mode control scheme applied to robot manipulators. International journal of Control, 45 (1987), 4, pp. 1197-1209
• [21] Utkin, V. I., Sliding mode control design principles and applications to electric drives, IEEE transactions on industrial electronics, 40 (1993), 1, pp. 23-36
• [22] Utkin, V. I., Chang, H. C., Sliding mode control in electro-mechanical systems, Mathematical Problems in Engeneering, 8 (2002), 4-5, pp. 451-473
• [23] Bartolini, G., Pisano, A., Punta, E., Usai, E., A survey of applications of second-order sliding mode control to mechanical systems. International Journal of control, 76 (2003), 9-10, pp. 875-892