Wednesday , October 4 2023

Adaptive PD-SMC for Nonlinear Robotic Manipulator Tracking Control

Tolgay KARA1, Ali Hussien MARY2
1 University of Gaziantep,
Department of Electrical and Electronics Engineering,
Gaziantep, 27310, Turkey.

2 University of Baghdad,
Al-Khwarizmi College of Engineering,
Baghdad, Iraq.

ABSTRACT: This paper presents an adaptive and robust control scheme, which is based on Sliding Mode Control (SMC) accompanied by Proportional Derivative (PD) control terms for trajectory tracking of nonlinear robotic manipulators in the presence of system uncertainties and external disturbances. Two important features make the proposed control method more suitable for tracking control of robotic manipulators in comparison with SMC. One of these features is the model free nature of proposed control, which implies avoiding the need to determine dynamic model of the controlled system. As a second feature, control and adaption technique used in the proposed method cancels the need for determining the upper bounds of uncertainties. It should be emphasized that SMC requires the dynamic model of the system and prior knowledge of upper bound of uncertainties. Lyapunov theory is used to prove stability of proposed method and a four link SCARA robot is selected for demonstrating efficacy of the proposed method via simulation tests. Simulation tests are utilized to compare the proposed method with conventional SMC in terms of tracking control performance and cumulative error. Results have revealed significant improvement in both aspects.

KEYWORDS: Manipulator dynamics, Sliding mode control, Robust control.


Tolgay KARA, Ali Hussien MARY,
Adaptive PD-SMC for Nonlinear Robotic Manipulator Tracking Control, Studies in Informatics and Control, ISSN 1220-1766, vol. 26(1), pp. 49-58, 2017.


  1. Ajwad, S. A., Iqbal, J., Khan, A. A. & Mehmood, A. (2015). Disturbance-observer-based robust control of a serial-link robotic manipulator using SMC and PBC techniques, Studies in Informatics and Control24 (4), 401-408.
  2. Astrom, J. & Wittenmark, B. (2008). Adaptive Control, Dover.
  3. Cerman, O. & Hušek, P. (2012). Adaptive fuzzy sliding mode control for electro-hydraulic servo mechanism, Expert Systems with Applications39 (11), 10269-10277.
  4. Chen, C. Y., Li, T. H. S., & Yeh, Y. C. (2009). EP-based kinematic control and adaptive fuzzy sliding-mode dynamic control for wheeled mobile robots, Information Sciences, 179 (1), 180-195.
  5. Craig, J. (2014). Introduction to robotics: mechanics and control, Addison Wesley Publishing, Massachusetts, MA, 171-192.
  6. D’Emilia, G., Marra, A., & Natale, E. (2007). Use of neural networks for quick and accurate auto-tuning of PID controller, Robotics and Computer-Integrated Manufacturing, 23 (2), 170-179.
  7. Ferrara, A., & Magnani, L. (2007). Motion control of rigid obot manipulators via first and second order sliding modes, Journal of Intelligent & Robotic Systems48 (1), 23-36.
  8. Ghosh, B. B., Sarkar, B. K., & Saha, R. (2015). Realtime performance analysis of different combinations of fuzzy–PID and bias controllers for a two degree of freedom electrohydraulic parallel manipulator, Robotics and Computer-Integrated Manufacturing, 34, 62-69.
  9. Kuo, C. L., Li, T. H. S., & Guo, N. R. (2005). Design of a novel fuzzy sliding-mode control for magnetic ball levitation system, Journal of Intelligent & Robotic Systems42 (3), 295-316.
  1. Lee, K. J., Choi, J. J., & Kim, J. S. (2004). A proportional-derivative-sliding mode hybrid control scheme for a robot manipulator, Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 218 (8), 667-674.
  2. Li, T. H. S., & Huang, Y. C. (2010). MIMO adaptive fuzzy terminal sliding-mode controller for robotic manipulators, Information Sciences, 180 (23), 4641-4660.
  3. Liang, Y. W., Xu, S. D., Liaw, D. C., & Chen, C. C. (2008). A study of T–S model-based SMC scheme with application to robot control, IEEE Transactions on Industrial Electronics, 55 (11), 3964-3971.
  4. Liu, Y., & Li, Y. (2005). Sliding mode adaptive neural-network control for nonholonomic mobile modular manipulators, Journal of Intelligent & Robotic Systems44 (3), 203-224.
  5. Ouyang, P. R., Acob, J., & Pano, V. (2014). PD with sliding mode control for trajectory tracking of robotic system, Robotics and Computer-Integrated Manufacturing30 (2), 189-200.
  6. Parnichkun, M., & Ngaecharoenkul, C. (2000). Hybrid of fuzzy and PID in kinematics control of a pneumatic system, In Industrial Electronics Society, 2000. IECON 2000. 26th Annual Conference of the IEEE (Vol. 2, 1485-1490).
  7. Parra-Vega, V., Arimoto, S., Liu, Y. H., Hirzinger, G., & Akella, P. (2003). Dynamic sliding PID control for tracking of robot manipulators: Theory and experiments, IEEE Transactions on Robotics and Automation19 (6), 967-976.
  8. Pratumsuwan, P., Thongchai, S., & Tansriwong, S. (2010). A hybrid of fuzzy and proportional-integral-derivative controller for electro-hydraulic position servo system, Energy Research Journal1 (2), 62-67.
  9. Roopaei, M., & Jahromi, M. Z. (2009). Chattering-free fuzzy sliding mode control in MIMO uncertain systems, Nonlinear Analysis: Theory, Methods & Applications, 71 (10), 4430-4437.
  10. Sadati, N., & Ghadami, R. (2008). Adaptive multi-model sliding mode control of robotic manipulators using soft computing, Neurocomputing, 71 (13), 2702-2710.
  11. Song, S., & Liu, W. (2006, November). Fuzzy parameters self-tuning PID control of switched reluctance motor based on Simulink/NCD, In Computational Intelligence for Modelling, Control and Automation, 2006 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (pp. 73-73).
  12. Su, Y. X., & Zheng, C. H. (2010). Global asymptotic tracking of robot manipulators with a simple decentralised non-linear PD-like controller, IET control theory & applications4 (9), 1605-1611.
  13. Voglewede, P., Smith, A. H., & Monti, A. (2009). Dynaic performance of a SCARA robot manipulator with uncertainty using polynomial chaos theory. IEEE Transactions on Robotics25 (1), 206-210.
  14. Van Pham, C., & Wang, Y. N. (2015). Robust adaptive trajectory tracking sliding mode control based on neural networks for cleaning and detecting robot manipulators, Journal of Intelligent & Robotic Systems79 (1), 101-114.
  15. Wang, H., Zhang, W., Tian, Y., & Qu, Q. (2015). Sliding Mode Control for Diesel Engine Using Extended State Observer, Studies in Informatics and Control24 (4), 439-448.
  16. Wai, R. J., Tu, C. Y., & Hsieh, K. Y. (2004). Adaptive tracking control for robot manipulator, International journal of systems science35 (11), 615-627.
  17. Yagiz, N., Hacioglu, Y., & Taskin, Y. (2008). Fuzzy sliding-mode control of active suspensions, IEEE Transactions  on industrial electronics55 (11), 3883-3890.
  18. Yorgancıoğlu, F., & Kömürcügil, H. (2008). Single-input fuzzy-like moving sliding surface approach to the sliding mode control, Electrical Engineering, 90 (3), 199-207.