Adaptive Fuzzy Control of Robot Manipulators with Asymptotic Tracking Performance

  • Sara Fateh
  • Mohammad Mehdi FatehEmail author


Adaptive fuzzy controllers are powerful intelligent control tools applied to handle the complexity of robot manipulators; however, their tracking performances are considerably affected by the fuzzy approximation error. This paper introduces a decentralized adaptive fuzzy control (AFC) design based on voltage control strategy, equipped with a novel fuzzy approximation error compensator. Asymptotic tracking performance is achieved by means of compensating the fuzzy approximation error adaptively. The proposed design simplifies the complex control procedure of electrically driven robot manipulators, and it is independent from the mathematical model. The asymptotic stability of the proposed controller has been guaranteed and proven via Barbalat’s lemma. The control approach is simulated on a four-link SCARA robot driven by permanent magnet DC motors, and the simulation results reveal the improvement in tracking the desired trajectory, compared to a previous AFC based on voltage control strategy without fuzzy approximation error compensation.


Adaptive fuzzy control Electrically driven SCARA robot Fuzzy approximation error Stability analysis 



  1. Ahmadi, S. M., & Fateh, M. M. (2018). Task-space control of robots using an adaptive Taylor series uncertainty estimator. International Journal of Control. Scholar
  2. Chaoui, H., & Gualous, H. (2017). Adaptive fuzzy logic control for a class of unknown nonlinear dynamic systems with guaranteed stability. Journal of Control Automation and Electrical Systems, 28, 727–736.CrossRefGoogle Scholar
  3. Chen, M., Chen, W. H., & Wu, Q. X. (2014). Adaptive fuzzy tracking control for a class of uncertain MIMO nonlinear systems using disturbance observer. Science China Information Sciences, 57, 1–13.zbMATHGoogle Scholar
  4. Fateh, M. M. (2008). On the voltage-based control of robot manipulators. International Journal of Control, Automation and Systems, 6, 702–712.Google Scholar
  5. Fateh, M. M. (2012). Robust control of flexible-joint robots using voltage control strategy. Nonlinear Dynamics, 67, 1525–1537.MathSciNetCrossRefGoogle Scholar
  6. Fateh, M. M., & Abedinzadeh, S. M. (2015). Adaptive fuzzy control of a mobile manipulator robot. Journal of Solid and Fluid Mechanics, 5(2), 17–27.Google Scholar
  7. Fateh, M. M., & Azargoshasb, S. (2014). Discrete-time indirect adaptive fuzzy control for robot manipulators. International Journal of Intelligent Computing and Cybernetics, 7, 382–396.MathSciNetCrossRefGoogle Scholar
  8. Fateh, M. M., & Fateh, S. (2012). Decentralized direct adaptive fuzzy control of robots using voltage control strategy. Nonlinear Dynamics, 70(3), 1919–1930.MathSciNetCrossRefGoogle Scholar
  9. Fateh, M. M., & Khoshdel, V. (2015). Voltage-based adaptive impedance force control for a lower-limb rehabilitation robot. Advanced Robotics, 29, 961–971.CrossRefGoogle Scholar
  10. Isidori, A. (1989). Nonlinear control systems. Heidelberg: Springer.CrossRefGoogle Scholar
  11. Krarti, M. (2010). Energy Audit of Building Systems: An Engineering Approach. Boca Raton: CRC Press.Google Scholar
  12. Li, Z., Chen, Z., & Chen, P. (2016). Disturbance observer-based fuzzy control of uncertain MIMO mechanical systems with input nonlinearities and its application to robotic exoskeleton. IEEE Transactions on Cybernetics, 47(4), 1–11.Google Scholar
  13. Li, T. H. S., & Huang, Y. C. (2010). MIMO adaptive fuzzy terminal sliding-mode controller for robotic manipulators. Information Sciences, 180, 4641–4660.MathSciNetCrossRefGoogle Scholar
  14. Mendes, N., & Neto, P. (2016). Indirect adaptive fuzzy control for industrial robots: A solution for contact applications. Expert Systems with Applications, 42, 8929–8935.CrossRefGoogle Scholar
  15. Moussaoui, S., Boulkroune, A., & Vaidyanathan, S. (2016). Fuzzy adaptive sliding-mode control scheme for uncertain underactuated systems. In: Advances and applications in nonlinear control systems (pp. 351–367).Google Scholar
  16. Patre, P. M., MacKunis, W., Kaiser, K., & Dixon, W. E. (2008). Asymptotic tracking for uncertain dynamic systems via a multilayer neural network feedforward and RISE feedback control structure. IEEE Transactions on Automatic Control, 53(9), 2180–2185.MathSciNetCrossRefGoogle Scholar
  17. Qu, Z., & Dawson, D. M. (1996). Robust tracking control of robot manipulators. New York: IEEE Press.zbMATHGoogle Scholar
  18. Shahnazi, R., & Akbarzadeh, M. (2008). PI adaptive fuzzy control with large and fast disturbance rejection for a class of uncertain nonlinear systems. IEEE Transactions on Fuzzy Systems, 16, 187–197.CrossRefGoogle Scholar
  19. Shi, W., Zhang, M., Guo, W., & Guo, L. (2011). Adaptive fuzzy control for MIMO nonlinear systems. Computers & Mathematics with Applications, 62, 2843–2853.MathSciNetCrossRefGoogle Scholar
  20. Slotine, J. J. E., & Li, W. (1991). Applied nonlinear control. Upper Saddle River: Prentice Hall.zbMATHGoogle Scholar
  21. Souzanchi-K, M., Arab, A., Akbarzadeh-T, M., & Fateh, M. M. (2017). Robust impedance control of uncertain mobile manipulators using time-delay compensation. IEEE Transactions on Control Systems Technology. Scholar
  22. Spong, M. W., Hutchinson, S., & Vidyasagar, M. (2006). Robot modelling and control. Hoboken: Wiley.Google Scholar
  23. Sun, W., Xia, J., Zhuang, G., Huang, X., & Shen, H. (2019). Adaptive fuzzy asymptotically tracking control of full state constrained nonlinear system based on a novel Nussbaum-type function. Journal of the Franklin Institute, 356, 1810–1827. Scholar
  24. Wang, L. X. (1996). A course in fuzzy systems and control. Englewood Cliffs: Prentice Hall.Google Scholar
  25. Wang, F., Chen, B., Liu, X., & Lin, C. (2018). Finite-time adaptive fuzzy tracking control design for nonlinear systems. IEEE Transactions on Fuzzy Systems, 26(3), 1207–1216.CrossRefGoogle Scholar
  26. Wu, J., Chen, W., & Li, J. (2015a). Fuzzy-approximation-based global adaptive control for uncertain strict-feedback systems with a priori known tracking accuracy. Fuzzy Sets and Systems, 273, 1–25.MathSciNetCrossRefGoogle Scholar
  27. Wu, T., & Juang, Y. (2008). Adaptive fuzzy sliding-mode controller of uncertain nonlinear systems. ISA Transactions, 47, 279–285.CrossRefGoogle Scholar
  28. Wu, T. S., Karkoub, M., Chen, H. S., Yu, W. S., & Her, M. (2015b). Robust tracking observer-based adaptive fuzzy control design for uncertain nonlinear MIMO systems with time delayed states. Information Sciences, 290, 86–105.CrossRefGoogle Scholar

Copyright information

© Brazilian Society for Automatics--SBA 2019

Authors and Affiliations

  1. 1.Department of Electrical and Robotic EngineeringShahrood University of TechnologyShahroodIran

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