Comparative Analysis of PID and Sliding Mode Control for a 3-DoF RRR Robot Arm

Abstract

This study presents a comparative analysis of Proportional-Integral-Derivative (PID) and Sliding Mode Control (SMC) methods applied to a custom-designed and 3D-printed 3-Degree-of-Freedom (3-DoF) Revolute-Revolute-Revolute (RRR) robotic manipulator. A central contribution of this work is the development of a dual-environment validation framework that integrates ROS Noetic with the Gazebo simulation platform, enabling seamless testing of controllers in both virtual and physical settings. This framework provides a practical pathway for bridging the gap between simulation-based evaluations and real-world experimentation, an aspect that remains underexplored in existing studies. The performance of both controllers is assessed through joint position errors, trajectory tracking accuracy, and torque demands for a cubic trajectory application. Experimental results show that while both controllers achieve satisfactory performance, SMC demonstrates superior trajectory tracking, with consistently lower Root Mean Square (RMS) errors across all joints. This improvement, however, is accompanied by slightly higher torque requirements compared to PID, highlighting the trade-off between enhanced accuracy and increased actuator effort. By combining a low-cost robotic platform with a reproducible dual-environment methodology, this study not only offers insights into the practical strengths and limitations of model-free PID and SMC but also establishes a framework that can inform future research and industrial applications.

Keywords

• Sliding Mode Control
• PID Control
• Robot Arm
• Trajectory Tracking
• Low-cost

References

1. [1]. Adebayo, RA, Obiuto, NC, Festus Ikhuoria, IC, Olajiga, OK. 2024. Robotics in manufacturing: A review of advances in automation and workforce implications. International Journal of Advanced Multidisciplinary Research and Studies; 4(2): 632 638.
2. [2]. Wang, X, Hou, B. 2021. Continuous time varying feedback control of a robotic manipulator with base vibration and load uncertainty. Journal of Vibration and Control; 27(3-4): 392 403.
3. [3]. Chen, S, Wen, JT. 2021. Industrial robot trajectory tracking control using multi layer neural networks trained by iterative learning control. Robotics; 10(1): 50.
4. [4]. Ashagrie, A, Salau, AO, Weldcherkos, T. 2021. Modeling and control of a 3 DOF articulated robotic manipulator using self tuning fuzzy sliding mode controller. Cogent Engineering; 8(1): 1950105.
5. [5]. Yin, X, Pan, L. 2018. Enhancing trajectory tracking accuracy for industrial robot with robust adaptive control. Robotics and Computer Integrated Manufacturing; 51: 97 102.
6. [6]. Utkin, VI. 1977. Variable structure systems with sliding modes. IEEE Transactions on Automatic Control; 22(2): 212 222.
7. [7]. Young, KD, Utkin, VI, Ozguner, U. 1999. A control engineer's guide to sliding mode control. IEEE Transactions on Control Systems Technology; 7(3): 328 342.
8. [8]. Mohammed, AA, Eltayeb, A. Dynamics and control of a two link manipulator using PID and sliding mode control. In 2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE), Khartoum, Sudan, 2018, pp 1 5.

9. [9]. Derbel, N, Ghommam, J, Zhu, Q (eds). Applications of Sliding Mode Control; Springer: Singapore, 2017; pp 335 337.
10. [10]. Singh, P, Prasad, L. 2020. A comparative performance analysis of PID control and sliding mode control of two link robot manipulator. International Research Journal on Advanced Science Hub; 2: 43 54.
11. [11]. Ohri, J, Vyas, DR, Topno, PN. Comparison of robustness of PID control and sliding mode control of robotic manipulator. In Proceedings of the International Symposium on Devices MEMS Intelligent Systems and Communication (ISDMISC), Sikkim, India, 2011, pp 5 10.
12. [12]. Loucif, F, Kechida, S. 2020. Optimization of sliding mode control with PID surface for robot manipulator by evolutionary algorithms. Open Computer Science; 10(1): 396 407.
13. [13]. Megalingam, RK, Rajendraprasad, A, Manoharan, SK. 2020. Comparison of planned path and travelled path using ROS navigation stack. In International Conference for Emerging Technology, Belgaum, India, 2020, pp 1 6.
14. [14]. Breiling, B, Dieber, B, Schartner, P. Secure communication for the robot operating system. In IEEE International Systems Conference, Montreal, QC, Canada, 2017, pp 1 6.
15. [15]. Quigley, M, Conley, K, Gerkey, B, et al. ROS: an open source Robot Operating System. In ICRA Workshop on Open-Source Software, Kobe, Japan, 2009, 3 3.2, p 5.
16. [16]. Qian, W, Xia, Z, Xiong, J, Gan, Y, Guo, Y, Weng, S, Zhang, J. Manipulation task simulation using ROS and Gazebo. In 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014), Bali, Indonesia, 2014, pp 2594 2598.
17. [17]. Deng, H, Xiong, J, Xia, Z. Mobile manipulation task simulation using ROS with MoveIt. In Proceedings of the 2017 IEEE International Conference on Real time Computing and Robotics (RCAR), Okinawa, Japan, 2017, pp 612 616.
18. [18]. Chen, J, Deng, H, Chai, W, Xiong, J, Xia, Z. Manipulation task simulation of a soft pneumatic gripper using ROS and Gazebo. In Proceedings of the 2018 IEEE International Conference on Real time Computing and Robotics (RCAR), Kandima, Maldives, 2018, pp 378 383.
19. [19]. Denavit, J, Hartenberg, RS. 1955. A kinematic notation for lower pair mechanisms based on matrices. Journal of Applied Mechanics; 22: 215 221.
20. [20]. Craig, J. Introduction to Robotics: Mechanics and Control; 4th edn. Pearson: Boston, USA, 2018.
21. [21]. Ogata, K. Modern Control Engineering; 5th ed. Prentice Hall: Upper Saddle River, NJ, 2010.
22. [22]. Tinoco, V, Silva, MF, Santos, FN, Morais, R, Magalhães, SA, Oliveira, PM. 2025. A review of advanced controller methodologies for robotic manipulators. International Journal of Dynamics and Control; 13(1): 1 17.
23. [23]. Soon, CC, Ghazali, R, Ghani, MF, Shern, CM, Sam, YM, Has, Z. 2022. Chattering analysis of an optimized sliding mode controller for an electro hydraulic actuator system. Journal of Robotics and Control (JRC); 3(2): 160 165.
24. [24]. Abukan, Y, Almalı, MN. 2023. Control of 2 DoF TRMS MIMO system using FOPID & FOSTSMC method. Journal of the Faculty of Engineering and Architecture of Gazi University; 38(1): 605 615.
25. [25]. Wang, J, Zhu, H, Zhang, C, Chen, Z, Huang, Y, Chen, W, et al. 2018. Adaptive hyperbolic tangent sliding mode control for building structural vibration systems for uncertain earthquakes. IEEE Access; 6: 74728 74736.
26. [26]. Shi, Z, Deng, C, Zhang, S, Xie, Y, Cui, H, Hao, Y. 2020. Hyperbolic tangent function based finite time sliding mode control for spacecraft rendezvous maneuver without chattering. IEEE Access; 8: 60838 60849.
27. [27]. Gu, J, Pan, S. 2024. Sliding mode control of dual active bridge converter based on hyperbolic tangent function. Journal of Physics: Conference Series; 2803(1): 012050.
28. [28]. Zhang, M, Zhang, Y, Chen, H, Cheng, X. 2019. Model independent PD SMC method with payload swing suppression for 3D overhead crane systems. Mechanical Systems and Signal Processing; 129: 381 393.
29. [29]. Fang, JS, Tsai, JSH, Yan, JJ, Guo, SM. 2019. Adaptive chattering free sliding mode control of chaotic systems with unknown input nonlinearity via smooth hyperbolic tangent function. Mathematical Problems in Engineering; 2019(1): 4509674.
30. [30]. Guo, L, Huangfu, Y, Ma, R. A novel high order sliding mode observer based on tanh function for a fuel cell UAV power system with uncertain disturbance. In 2019 IEEE Industry Applications Society Annual Meeting, Baltimore, MD, USA, 2019, pp 1 7.
31. [31]. Guan, M, Qu, C, Lv, J, Yang, L, Li, X. 2024. A novel RBF neural network–based sliding mode controller for a master–slave motor coordinated drive system. The International Journal of Advanced Manufacturing Technology; 133(9): 4907 4921.
32. [32]. Slotine, J-JE, Li, W. Applied Nonlinear Control; Prentice Hall: Englewood Cliffs, NJ, 1991; pp 276 278.
33. [33]. Khalil, HK. Nonlinear Systems; Prentice Hall: Upper Saddle River, NJ, 2002; pp 552 575.