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Nonlinear Tracking Control with Reduced Complexity of Serial Robots: A Robust Fuzzy Descriptor Approach

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Abstract

This paper presents a nonlinear tracking control approach for a two-degree-of-freedom serial manipulator. The design goal is to achieve a guaranteed \(\mathcal {H}_{\infty }\) tracking performance while keeping the designed controller as simple as possible for real-time implementation. To this end, the descriptor Takagi–Sugeno fuzzy modeling is used to describe the nonlinear dynamics of the robot. Then, based on Lyapunov stability theory, we propose conditions to design fuzzy controllers for trajectory tracking purposes. The control design procedure is reformulated as an optimization problem under linear matrix inequality constraints which can be effectively solved with semidefinite programming technique. Numerical experiments carried out with the Simscape Multibody™ library of MATLAB® clearly demonstrate the effectiveness of the proposed approach in terms of tracking control and numerical simplicity.

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References

  1. Lu, X.-G., Liu, M., Liu, J.-X.: Design and optimization of interval type-2 fuzzy logic controller for delta parallel robot trajectory control. Int. J. Fuzzy Syst. 19(1), 190–206 (2017)

    Article  Google Scholar 

  2. Pan, Y., Wang, H., Li, X., Yu, H.: Adaptive command-filtered backstepping control of robot arms with compliant actuators. IEEE Trans. Control Syst. Technol. 26(3), 1149–1156 (2018)

    Article  Google Scholar 

  3. Nojavanzadeh, D., Badamchizadeh, M.: Adaptive fractional-order non-singular fast terminal sliding mode control for robot manipulators. IET Control Theory Appl. 10(13), 1565–1572 (2016)

    Article  MathSciNet  Google Scholar 

  4. Tsai, C.-C., Cheng, M.-B., Lin, S.-C.: Robust tracking control for a wheeled mobile manipulator with dual arms using hybrid sliding-mode neural network. Asian J. Control 9(4), 377–389 (2007)

    Article  MathSciNet  Google Scholar 

  5. Huang, H.-C., Chiang, C.-H.: Backstepping holonomic tracking control of wheeled robots using an evolutionary fuzzy system with qualified ant colony optimization. Int. J. Fuzzy Syst. 18(1), 28–40 (2016)

    Article  MathSciNet  Google Scholar 

  6. Ouyang, P., Acob, J., Pano, V.: PD with sliding mode control for trajectory tracking of robotic system. Robot. Comput. Integr. Manuf. 30(2), 189–200 (2014)

    Article  Google Scholar 

  7. Hedberg, E., Norrlöf, M., Moberg, S., Gunnarsson, S.: Comparing feedback linearization and Jacobian linearization for LQ control of an industrial manipulator. In: Proceedings 12th IFAC Symposium Robot Control (2018)

  8. Pi, Y., Wang, X.: Trajectory tracking control of a 6-DOF hydraulic parallel robot manipulator with uncertain load disturbances. Control Eng. Pract. 19(2), 185–193 (2011)

    Article  Google Scholar 

  9. Jafarov, E.M., Parlakci, M.A., Istefanopulos, Y.: A new variable structure PID-controller design for robot manipulators. IEEE Trans. Control Syst. Technol. 13(1), 122–130 (2005)

    Article  Google Scholar 

  10. Purwar, S., Kar, I.N., Jha, A.N.: Adaptive output feedback tracking control of robot manipulators using position measurements only. Expert Syst. Appl. 34(4), 2789–2798 (2008)

    Article  Google Scholar 

  11. Hu, Q., Xu, L., Zhang, A.: Adaptive backstepping trajectory tracking control of robot manipulator. J. Franklin Inst. 349(3), 1087–1105 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  12. Pan, Y., Yu, H.: Composite learning robot control with guaranteed parameter convergence. Automatica 89, 398–406 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  13. Tseng, C.-S., Chen, B.-S., Uang, H.-J.: Fuzzy tracking control design for nonlinear dynamic systems via T–S fuzzy model. IEEE Trans. Fuzzy Syst. 9(3), 381–392 (2001)

    Article  Google Scholar 

  14. Nguyen, A.-T., Dambrine, M., Lauber, J.: Lyapunov-based robust control design for a class of switching non-linear systems subject to input saturation: application to engine control. IET Control Theory Appl. 8, 1789–1802 (2014)

    Article  MathSciNet  Google Scholar 

  15. Vermeiren, L., Dequidt, A., Afroun, M., Guerra, T.-M.: Motion control of planar parallel robot using the fuzzy descriptor system approach. ISA Trans. 51(5), 596–608 (2012)

    Article  Google Scholar 

  16. Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man, Cybern. B, Cybern. SMC–15(1), 116–132 (1985)

    Article  MATH  Google Scholar 

  17. Tanaka, K., Wang, H.O.: Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach. John Wiley & Sons, Hoboken (2004)

    Google Scholar 

  18. Zhang, H., Liu, D.: Fuzzy Modeling and Fuzzy Control. Birkhauser, Basel (2006)

    MATH  Google Scholar 

  19. Lilly, J.H.: Fuzzy Control and Identification. John Wiley & Sons Inc, Hoboken (2010)

    Book  MATH  Google Scholar 

  20. Lendek, Z., Guerra, T.M., Babuška, R., De Schutter, B.: Stability Analysis and Nonlinear Observer Design Using Takagi–Sugeno Fuzzy Models, vol. 262. Springer, Berlin, Heidelberg (2011)

    Book  MATH  Google Scholar 

  21. Nguyen, A.-T., Taniguchi, T., Eciolaza, L., Campos, V., Palhares, R., Sugeno, M.: Fuzzy control systems: past, present and future. IEEE Comput. Intell. Mag. 14(1), 56–68 (2019)

    Article  Google Scholar 

  22. Nguyen, A.-T., Sentouh, C., Popieul, J.-C.: Driver-automation cooperative approach for shared steering control under multiple system constraints: design and experiments. IEEE Trans. Ind. Electron. 64(5), 3819–3830 (2017)

    Article  Google Scholar 

  23. Chen, B.-S., Wu, C.-H.: Robust optimal reference-tracking design method for stochastic synthetic biology systems: T–S fuzzy approach. IEEE Trans. Fuzzy Syst. 18(6), 1144–1159 (2010)

    Article  Google Scholar 

  24. Hu, X., Wu, L., Hu, C., Gao, H.: Fuzzy guaranteed cost tracking control for a flexible air-breathing hypersonic vehicle. IET Control Theory Appl. 6(9), 1238–1249 (2012)

    Article  MathSciNet  Google Scholar 

  25. Hung, C.-Y., Liu, P., Lian, K.-Y.: Fuzzy virtual reference model sensorless tracking control for linear induction motors. IEEE Trans. Cybern. 43(3), 970–981 (2013)

    Article  Google Scholar 

  26. Taniguchi, T., Tanaka, K., Wang, H.O.: Fuzzy descriptor systems and nonlinear model following control. IEEE Trans. Fuzzy Syst. 8(4), 442–452 (2000)

    Article  Google Scholar 

  27. Spong, M.W.: Modeling and control of elastic joint robots. J. Dyn. Syst. Meas. Control 109(4), 310–318 (1987)

    Article  MATH  Google Scholar 

  28. Andersson, S., Söderberg, A., Björklund, S.: Friction models for sliding dry, boundary and mixed lubricated contacts. Tribol. Int. 40(4), 580–587 (2007)

    Article  Google Scholar 

  29. Makarov, M., Grossard, M., Rodríguez-Ayerbe, P., Dumur, D.: Modeling and preview \(H_\infty \) control design for motion control of elastic-joint robots with uncertainties. IEEE Trans. Ind. Electron. 63(10), 6429–6438 (2016)

    Article  Google Scholar 

  30. van der Schaft, A.J.: \(\cal{L}_2-\)gain analysis of nonlinear systems and nonlinear state-feedback \(\cal{H}_\infty \) control. IEEE Trans. Autom. Control 37(6), 770–784 (1992)

    Article  Google Scholar 

  31. Guerra, T.M., Bernal, M., Guelton, K., Labiod, S.: Non-quadratic local stabilization for continuous-time Takagi–Sugeno models. Fuzzy Sets Syst. 201, 40–54 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  32. Nguyen, A.-T., Márquez, R., Guerra, T.-M., Dequidt, A.: Improved lmi conditions for local quadratic stabilization of constrained Takagi–Sugeno fuzzy systems. Int. J. Fuzzy Syst. 19(1), 225–237 (2017)

    Article  MathSciNet  Google Scholar 

  33. Nguyen, A.-T., Márquez, R., Dequidt, A.: An augmented system approach for LMI-based control design of constrained Takagi–Sugeno fuzzy systems. Eng. Appl. Artif. Intell. 61, 96–102 (2017)

    Article  Google Scholar 

  34. Löfberg, J.: YALMIP: A toolbox for modeling and optimization in MATLAB, In: IEEE Int. Symp. Comput. Aided Control Syst. Des., Taipei, pp. 284–289 (2004)

  35. Toh, K., Todd, M., Tutuncu, R.: SDPT3—a Matlab software package for semidefinite programming, version 1.3. Optim. Methods Softw. 11, 545–581 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  36. Nguyen, A.-T., Sentouh, C., Popieul, J.: Sensor reduction for driver-automation shared steering control via an adaptive authority allocation strategy. IEEE/ASME Trans. Mechatron. 23(1), 5–16 (2018)

    Article  Google Scholar 

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Correspondence to Anh-Tu Nguyen.

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Nguyen, VA., Nguyen, AT., Dequidt, A. et al. Nonlinear Tracking Control with Reduced Complexity of Serial Robots: A Robust Fuzzy Descriptor Approach. Int. J. Fuzzy Syst. 21, 1038–1050 (2019). https://doi.org/10.1007/s40815-019-00613-1

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  • DOI: https://doi.org/10.1007/s40815-019-00613-1

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