DYNAMIC MODEL AND CONTROL OF 2-DOF ROBOTIC ARM

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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [16] Hazewinkel, M., Lagrange equations (in mechanics)-Encyclopedia of Mathematics, Springer, Netherlands, 1990
17. [17] Abut, T., Modeling and Optimal Control of a DC Motor. Int. J. Eng. Trends Technol., 32 (2016), 3, pp. 146-150
18. [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. [19] Aström, K. J., Hägglund, T., PID controllers: theory, design, and tuning, Instrument Society of USA, 1995
20. [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. [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. [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. [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