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Fuzzy neural network and observer-based fault-tolerant adaptive nonlinear control of uncertain 5-DOF upper-limb exoskeleton robot for passive rehabilitation

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Abstract

This paper investigates the control of a 5-DOF upper-limb exoskeleton robot used for passive rehabilitation therapy. The robot is subject to uncertain dynamics, disturbance torques, unavailable full-state measurement, and different types of actuation faults. An adaptive nonlinear control scheme, which uses a new reaching law-based sliding mode control strategy, is proposed. This scheme incorporates a high-gain state observer with dynamic high-gain matrix and a fuzzy neural network (FNN) for state vector and nonlinear dynamics estimation, respectively. Using dynamic parameters, the scheme provides an efficient mean for simultaneously tackling the effects of FNN approximation errors, disturbance torques and actuation faults without any prior bounds knowledge and fault detection and diagnosis components. Using simulation results, it is shown that with the presented scheme, faster response, fewer oscillations during transient phase, good tracking accuracy, and chattering-free control torques with lower amplitudes are obtained.

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References

  1. Kang, H.-B., Wang, J.-H.: Adaptive robust control of 5 DOF upper-limb exoskeleton robot. Int. J. Control Autom. Syst. 13(3), 733–741 (2015)

    Article  Google Scholar 

  2. Kang, H.-B., Wang, J.-H.: Adaptive control of 5 DOF upper-limb exoskeleton robot with improved safety. ISA Trans. 52(3), 844–852 (2013)

    Article  Google Scholar 

  3. Ozkul, F., Barkana, D.E.: Design and control of an upper limb exoskeleton robot RehabRoby. In: TAROS 2011. LNAI 6856, pp. 125–136. Springer, Heidelberg (2011)

  4. Anam, K., Al-Jumaily, A.A.: Active exoskeleton control systems: state of the art. In: International Symposium on Robotics and Intelligent Sensors, vol. 41, pp. 107–123. Procedia Engineering (2012)

  5. Pons, J.L.: Wearable Robots: Biomechatronics Exoskeletons, vol. 70. Wiley, Hoboken (2008)

    Book  Google Scholar 

  6. Perry, J.C., Rosen, J., Burns, S.: Upper-limb powered exoskeleton design. IEEE/ASME Trans. Mechatron. 12(4), 408–417 (2007)

    Article  Google Scholar 

  7. Hendricks, H.T., van Limbeek, J., Guerts, A.C., Zwarts, M.J.: Motor recovery after stroke: a systematic review. Arch. Phys. Med. Rehabil. 83(11), 1629–1637 (2002)

    Article  Google Scholar 

  8. Nef, T., Nihelj, M., Riener, R.: ARMin: a robot for patient cooperative arm therapy. Med. Biol. Eng. Comput. 45, 887–900 (2007)

    Article  Google Scholar 

  9. Khan, A.M., Yun, D.-W., Ali, M.A., Zuhaib, K.M., Yuan, C., Iqbal, J., Han, J., Shin, K., Han, C.: Passivity based adaptive control for upper extremity assist exoskeleton. Int. J. Control Autom. Syst. 14(1), 291–300 (2016)

    Article  Google Scholar 

  10. Sandoval-Gonzalez, O., Jacinto-Villegas, J., Herrera-Aguilar, I., Portillo-Rodriguez, O., Tripicchio, P., Hernandez-Ramos, M., Flores-Cuautle, A., Avizzano, C.: Design and development of a hand exoskeleton robot for active and passive rehabilitation. Int. J. Adv. Robot Syst. (2016). doi:10.5772/62404

    Google Scholar 

  11. Tong, S., Wang, T., Li, Y.: Fuzzy adaptive actuator failure compensation control of uncertain stochastic nonlinear systems with unmodeled dynamics. IEEE Trans. Fuzzy Syst. 22(3), 563–574 (2014)

    Article  Google Scholar 

  12. Li, Y., Tong, S., Li, T.: Adaptive fuzzy output-feedback control for output constrained nonlinear systems in the presence of input saturation. Fuzzy Sets Syst. 248, 138–155 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  13. Tong, S., Min, C., Jun, Z.: Fuzzy adaptive output tracking control of nonlinear systems. In: 1999 IEEE International Fuzzy Systems Conference Proceedings. IEEE, Seoul (1999)

  14. Tong, S., Huo, B., Li, Y.: Observer-based adaptive decentralized fuzzy fault-tolerant control of nonlinear large-scale systems with actuator failures. IEEE Trans. Fuzzy Syst. 22(1), 1–15 (2014)

    Article  Google Scholar 

  15. Li, Y., Tong, S.: Prescribed performance adaptive fuzzy output-feedback dynamic surface control for nonlinear large-scale systems with time delays. Inf. Sci. 292, 125–142 (2015)

    Article  MathSciNet  Google Scholar 

  16. Márton, L.: Actuator fault diagnosis in mechanical systems-fault power estimation approach. Int. J. Control Autom. Syst. 13(1), 110–119 (2015)

    Article  Google Scholar 

  17. Brambilla, D., Capisani, L.M., Ferrara, A., Pisu, P.: Fault detection for robot manipulators via second-order sliding modes. IEEE Trans. Ind. Electron. 55(11), 3954–3963 (2008)

    Article  Google Scholar 

  18. Veluvolu, K.C., Defoort, M., Soh, Y.C.: High-gain observer with sliding mode for nonlinear state estimation and fault reconstruction. J. Frankl. Inst. 351, 1995–2014 (2014)

    Article  MathSciNet  Google Scholar 

  19. Veluvolu, K.C., Kim, M.Y., Lee, D.: Nonlinear sliding mode high-gain observers for fault estimation. Int. J. Syst. Sci. 42, 1065–1074 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  20. Gholami, A., Markazi, A.H.D.: A new adaptive fuzzy sliding mode observer for a class of MIMO nonlinear systems. Nonlinear Dyn. 70, 2095–2105 (2012)

    Article  MathSciNet  Google Scholar 

  21. Goléa, N., Goléa, A., Barra, K., Bouktir, T.: Observer-based adaptive control of robot manipulators: fuzzy systems approach. Appl. Soft Comput. 8, 778–787 (2008)

    Article  Google Scholar 

  22. Liu, Y.-J., Tong, S.-C., Wang, W., Li, Y.-M.: Observer-based direct adaptive fuzzy control of uncertain nonlinear systems and its applications. Int. J. Control Autom. Syst. 7(4), 681–690 (2009)

    Article  Google Scholar 

  23. Bugarian, A., Miranda, W., Forner-Cordero, A.: Upper limb exoskeleton control based on sliding mode control and feedback linearization. In: 2013 ISSNIP Biosignals and Biorobotics Conference (BRC), pp. 1–6. IEEE, Rio de Janeiro (2013)

  24. Wu, Q., Wang, X., Du, F., Zhu, Q.: Fuzzy sliding mode control of an upper limb exoskeleton for robot-assisted rehabilitation. In: 2015 International Symposium on Medical Measurements and Applications, pp. 451–456. IEEE, Turin (2015)

  25. Liu, J., Wang, X.: Advanced Sliding Mode Control for Mechanical Systems. Tsinghua University Press, Beijing (2011)

    Book  Google Scholar 

  26. Yin, C., Chen, Y., Zhong, S.: Fractional-order sliding mode based extremum seeking control of a class of nonlinear systems. Automatica 50(12), 3173–3181 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  27. Yin, C., Cheng, Y., Chen, Y., Stark, B., Zhong, S.: Adaptive fractional-order switching-type control method design for 3D fractional-order nonlinear systems. Nonlinear Dyn. 82, 39–52 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  28. Yin, C., Stark, B., Chen, Y., Zhong, S., Lau, E.: Fractional-order adaptive minimum energy cognitive lighting control strategy for the hybrid lighting system. Energy Build. 87, 176–184 (2015)

    Article  Google Scholar 

  29. Zhao, X., Shi, P., Zheng, X., Zhang, L.: Adaptive tracking control for switched stochastic nonlinear systems with unknown actuator dead-zone. Automatica 60, 193–200 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  30. Zhao, X., Shi, P., Zheng, X., Zhang, J.: Intelligent tracking control for a class of uncertain high-order nonlinear systems. IEEE Trans. Neural Netw. Learn. Syst. (2015). doi:10.1109/TNNLS.2015.2460236

    Google Scholar 

  31. Rahman, M.H., Saad, M., Kenné, J.P., Archambault, P.S.: Exoskeleton robot for rehabilitation of elbow and forearm movements. In: 18th Mediterranean Conference on Control and Automation Congress. IEEE, Marrakech (2010)

  32. Rahman, M.H., Archambault, P.S., Saad, M., Luna, C.O., Ferrer, S.B.: Robot aided passive rehabilitation using nonlinear control techniques. In: 9th Asian Control Conference (ASCC). IEEE, Istanbul (2013)

  33. Babaiasl, M., Goldar, S.N., Barhaghtalab, M.H., Meigoli, V.: Sliding mode control of an exoskeleton robot for use in upper-limb rehabilitation. In: Proceedings of the 3rd RSI International Conference on Robotics and Mechatronics. IEEE, Tehran (2015)

  34. Soltanpour, M.R., Khooban, M.H.: A particle swarm optimization approach for fuzzy sliding mode control for tracking the robot manipulator. Nonlinear Dyn. 74, 467–478 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  35. Ju, M.-S., Lin, C.-C.K., Lin, D.-H., Hwang, I.-S., Chen, S.-M.: A rehabilitation robot with force-position hybrid fuzzy controller: hybrid fuzzy control of rehabilitation robot. IEEE Trans. Neural Syst. Rehabil. Eng. 13(3), 349–358 (2005)

    Article  Google Scholar 

  36. Denève, A., Moughamir, S., Afilal, L., Zayton, J.: Control system design of a 3-DOF upper limbs rehabilitation robot. Comput. Methods Programs Biomed. 89, 202–214 (2008)

    Article  Google Scholar 

  37. Komada, S., Hashimoto, Y., Okuyama, N., Hisada, T., Hirai, J.: Development of a biofeedback therapeutic-exercise-supporting manipulator. IEEE Trans. Ind. Electron. 56(10), 3914–3920 (2009)

    Article  Google Scholar 

  38. Dombre, E.: Analyse et modélisation des robots manipulateurs. Hermès Publications, Paris (2001)

    Google Scholar 

  39. Rahman, M.H., Saad, M., Kenné, J.P., Archambalt, P.S.: Nonlinear sliding mode control implementation of an upper limb exoskeleton robot to provide passive rehabilitation therapy. In: ICIRA 2012. Part II, LNAI 7507, pp. 52–62. Springer, Berlin (2012)

  40. Rahman, M.H., Saad, M., Kenné, J.P., Archambalt, P.S.: Control of an exoskeleton robot arm with sliding mode exponential reaching law. Int. J. Control Autom. Syst. 11(1), 92–104 (2013)

    Article  Google Scholar 

  41. Xin, M., Fei, J.: An adaptive fuzzy sliding mode controller for MEMS triaxial gyroscope with angular velocity estimation. Nonlinear Dyn. (2012). doi:10.1007/s11071-012-0433-z

    MathSciNet  Google Scholar 

  42. Du, H., Yu, X., Chen, M.Z.Q., Li, S.: Chattering-free discrete-time sliding mode control. Automatica 68(6), 81–91 (2016)

    MathSciNet  MATH  Google Scholar 

  43. Khalil, H.K.: Nonlinear Systems, 3rd edn. Prentice Hall, Upper Saddle River (2002)

    MATH  Google Scholar 

  44. Fallaha, C.J., Saad, M., Kanaan, H.Y., Al-Haddad, K.: Sliding-mode robot control with exponential reaching law. IEEE Trans. Ind. Electron. 58(2), 600–610 (2011)

    Article  Google Scholar 

  45. De Luca, A., Mattone, R.: An identification scheme for robot actuator faults. In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1127–1131. IEEE (2005)

  46. Caccavale, F., Cilibrizzi, P., Villani, L.: Actuators fault diagnosis for robot manipulators with uncertain model. Control Eng. Pract. 17(1), 146–157 (2009)

    Article  Google Scholar 

  47. Asada, H., Youcef-Toumi, K.: Analysis and design of a direct-drive arm with five-bar-link parallel drive mechanism. ASME J. Dyn. Syst. Meas. Control 106(3), 225–230 (1984)

    Article  Google Scholar 

  48. Perruquetti, W., Barbot, J.P.: Sliding Mode Control in Engineering. Marcel Dekker, Inc., New York (2002)

    Book  Google Scholar 

  49. Slotine, J.J.E., Li, W.: Applied Nonlinear Control. Prentice-Hall, Engelwood Cliffs (1991)

    MATH  Google Scholar 

  50. Aschmann, H., Schindele, D.: Sliding-mode control of a high-speed linear axis driven by pneumatic muscle actuators. IEEE Trans. Ind. Electron. 55(11), 3855–3864 (2008)

    Article  Google Scholar 

  51. Orlowska-Kowalska, T., Kaminski, M., Szabat, K.: Implementation of a sliding-mode controller with an integral function and fuzzy gain value for electrical drive with an elastic joint. IEEE Trans. Ind. Electron. 57(4), 1309–1317 (2010)

    Article  Google Scholar 

  52. Kawamura, A., Ito, H., Sakamoto, K.: Chattering reduction of disturbance observer based sliding mode control. IEEE Trans. Ind. Appl. 30(2), 456–461 (1994)

    Article  Google Scholar 

  53. Tai, N., Ahn, K.: A RBF neural network sliding mode controller for SMA actuator. Int. J. Control Autom. Syst. 8(6), 1296–1305 (2010)

    Article  Google Scholar 

  54. Gao, W.B., Hung, J.C.: Variable structure control of nonlinear-systems—a new approach. IEEE Trans. Ind. Electron. 40(1), 45–55 (1993)

    Article  Google Scholar 

  55. Rong, H.-J.: Indirect adaptive fuzzy-neural control of robot manipulator. In: 2012 IEEE 9th International Conference on High Performance Computing and Communications and 2012 IEEE 14th International Conference on Embedded Software and Systems (HPCC-ICESS). IEEE, Liverpool (2012)

  56. Kiguchi, K., Hayashi, Y.: An EMG-based control for an upper-limb power-assist exoskeleton robot. IEEE Trans. Syst. Man Cybern. Part B Cybern. 42(4), 1064–1071 (2012)

    Article  Google Scholar 

  57. Gunasekara, J.M.P., Gopura, R.A.R.C, Jayawardane, T.S.S., Lalitharathne, S.W.H.M.T.D.: Control methodologies for upper limb exoskeleton robots. In: 2012 IEEE/SICE International Symposium on System Integration (SII). IEEE, Fukuoka (2012)

  58. Pratap, B., Purwar, S.: Sliding mode state observer for 2-DOF twin rotor MIMO system. In: 2010 International Conference on Power. Control and Embedded Systems (ICPCES), pp. 1–6. IEEE, Allahabad (2010)

  59. Veluvolu, K.C., Zhe, F., Soh, Y.C.: Nonlinear sliding mode high-gain observers for fault detection. In: 2010 International Workshop on Variable Structure Systems, pp. 203–208. IEEE, Mexico City (2010)

  60. Veluvolu, K.C., Soh, Y.C., Cao, W.: Robust observer with sliding mode estimation for nonlinear uncertain systems. IET Control Theory Appl. 1(5), 1533–1540 (2007)

  61. Fuller, R.: Neural Fuzzy Systems. Abo Akademi University, Abo (1995)

    Google Scholar 

  62. Mushage, B.O., Chedjou, J.C., Kyamakya, K.: An extended neuro-fuzzy-based robust adaptive sliding mode controller for linearizable systems and its applications on a new chaotic system. Nonlinear Dyn. 83, 1601–1619 (2016)

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  1. Institute of Smart Systems Technologies, University of Klagenfurt, Klagenfurt, Austria

    Baraka Olivier Mushage, Jean Chamberlain Chedjou & Kyandoghere Kyamakya

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  1. Baraka Olivier Mushage
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  2. Jean Chamberlain Chedjou
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Mushage, B.O., Chedjou, J.C. & Kyamakya, K. Fuzzy neural network and observer-based fault-tolerant adaptive nonlinear control of uncertain 5-DOF upper-limb exoskeleton robot for passive rehabilitation. Nonlinear Dyn 87, 2021–2037 (2017). https://doi.org/10.1007/s11071-016-3173-7

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