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Active Fault-Tolerant Control Strategy for Electromechanical Servo System Based on Dual Fuzzy RBF Neural Networks and Velocity Reconstruction

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Abstract

In this paper, based on dual fuzzy RBF neural networks and velocity reconstruction, a novel active fault-tolerant control strategy is proposed. Firstly, considering the excellent approximation ability of fuzzy NNs to nonlinear function, based on dual fuzzy RBF NNs, this paper puts forward a novel fault diagnosis observer, which is used to accurately estimate additive faults and disturbances, additive faults and disturbances can be compensated in the proposed controller by applying feedforward cancelation technique to realize the fault-tolerant control. At the same time, considering the velocity sensor fault may be occur in practical electromechanical servo system, so the designed fault diagnosis observer can also be used to estimate the velocity of the system, so as to realize the velocity reconstruction in controller when velocity sensor fault occurs. Secondly, in order to improve the response speed and tracking accuracy of electromechanical servo system, a fractional-order integral sliding mode controller is proposed, which combined integral sliding mode control with fractional-order calculus theory to improve the transient response performance of integral sliding mode control. By applying the proposed fault diagnosis observer and fractional-order integral sliding mode controller, the proposed active fault-tolerant control for electromechanical servo system is realized.

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References

  1. Cai, W., Liao, X.H., Song, Y.D.: Indirect robust adaptive fault-tolerant control for attitude tracking of spacecraft. J. Guid. Control. Dyn. 31(5), 1456–1463 (2008). https://doi.org/10.2514/1.31158

    Article  Google Scholar 

  2. Vanchinathan, K., Valluvan, K.R., Gnanavel, C., Gokul, C., Albert, J.R.: An improved incipient whale optimization algorithm based robust fault detection and diagnosis for sensorless brushless DC motor drive under external disturbances. Int. Trans. Electr. Energy Syst. 31, e13251 (2021). https://doi.org/10.1002/2050-7038.13251

    Article  Google Scholar 

  3. Bajpai, G., Chang, B.C., Lau, A.: Reconfiguration of flight control systems for actuator failures. IEEE Aerosp. Electron. Syst. Mag. 16(9), 29–33 (2001). https://doi.org/10.1109/DASC.2000.886932

    Article  Google Scholar 

  4. Kim, K.S., Lee, K.J., Kim, Y.: Reconfigurable flight control system design using direct adaptive method. J. Guid. Control Dyn. 26(4), 543–550 (2003)

    Article  Google Scholar 

  5. Yen, G.G., Ho, L.W.B.T.-I.I.S. on I.C.: On-line model-based fault diagnosis and accommodation. In: Proceeding of the 2001 IEEE International Symposium on Intelligent Control, pp.73–78(2001)

  6. Gao, Z.Q., Antsaklis, P.J.: Stability of the pseudo-inverse method for reconfigurable control systems. Int. J. Control 53(3), 717–729 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  7. Cao, F., Sun, H., Li, Y., et al.: Fuzzy adaptive fault-tolerant control for a class of active suspension systems with time delay. Int. J. Fuzzy Syst. 21, 2054–2065 (2019). https://doi.org/10.1007/s40815-019-00719-6

    Article  MathSciNet  Google Scholar 

  8. Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359–366 (1989)

    Article  MATH  Google Scholar 

  9. Bahi, J.M., Contassot-Vivier, S., Sauget, M., Vasseur, A.B.T.-I.C. on H.P.C. for C.S.: A parallel incremental learning algorithm for neural networks with fault tolerance. In: 8th International Conference on High Performance Computing for Computational Science, Vol. 5336, pp. 174–187(2008)

  10. Skaf, Z., Wang, H., Guo, L.: Fault tolerant control based on stochastic distribution via RBF neural networks. Syst. Eng. Electron. J. 22(1), 63–69 (2011)

    Article  Google Scholar 

  11. Song, Q., Song, Y.D.: Data-based fault-tolerant control of high-speed trains with traction /braking notch nonlinearities and actuator failures. IEEE Trans. Neural Networks. 22(12), 2250–2261 (2011)

    Article  Google Scholar 

  12. Zhou, X., Tian, Y., Wang, H.: Neural network state observer-based robust adaptive fault-tolerant quantized iterative learning control for the rigid-flexible coupled robotic systems with unknown time delays. Appl. Math. Comput. 430, 127286 (2022). https://doi.org/10.1016/j.amc.2022.127286

  13. Wu, J., Yang, Y.: Neuroadaptive Regulation for Uncertain Systems with Quantized States and Sensor Faults. IEEE Trans. Circuits Syst. II 69, 3199–3203 (2022). https://doi.org/10.1109/TCSII.2022.3142145

  14. Li, Y., Hu, Q., Shao, X.: Neural network-based fault diagnosis for spacecraft with single-gimbal control moment gyros. Chin. J. Aeronaut. 35, 261–273 (2022). https://doi.org/10.1016/j.cja.2021.11.020

    Article  Google Scholar 

  15. Zhang, G., Qiu, S., Wang, F.: Adaptive fuzzy fault-tolerant control of flexible spacecraft with rotating appendages. Int. J. Fuzzy Syst. (2022). https://doi.org/10.1007/s40815-022-01338-4

    Article  Google Scholar 

  16. Albert, J.R., Sharma, A., Rajani, B., Mishra, A., Saxena, A., Nandagopal, C., Mewada, S.: Investigation on load harmonic reduction through solar-power utilization in intermittent SSFI using particle swarm, genetic, and modified firefly optimization algorithms. J. Intell. Fuzzy Syst. 42, 4117–4133 (2022). https://doi.org/10.3233/JIFS-212559

    Article  Google Scholar 

  17. Gnanavel, C., Muruganantham, P., Vanchinathan, K., Johny Renoald, A.: Experimental Validation and Integration of Solar PV Fed Modular Multilevel Inverter (MMI) and Flywheel Storage System. In: 2021 IEEE Mysore Sub Section International Conference, MysuruCon 2021 (2021). https://doi.org/10.1109/MysuruCon52639.2021.9641650.

  18. Santhiya, K., Devimuppudathi, M., Kumar, D.S., Renold, A.J.: Real time speed control of three phase induction motor by using lab view with fuzzy logic. J. Sci. Eng. Technol. 5(2) (2018)

  19. Dhivya, M., Renoald, A.J.: Fuzzy grammar based hybrid split-capacitors and split inductors applied in positive output Luo-converters. Int. J. Sci. Res. Sci. Eng. Technol. 3, 327–332 (2017)

    Google Scholar 

  20. Márquez-Vera, M.A., Ramos-Velasco, L.E., López-Ortega, O., Zúñiga-Peña, N.S., Ramos-Fernández, J.C., Ortega-Mendoza, R.M.: Inverse fuzzy fault model for fault detection and isolation with least angle regression for variable selection. Comput. Ind. Eng. 159, 107499 (2021). https://doi.org/10.1016/j.cie.2021.107499

    Article  Google Scholar 

  21. Su, H., Zhang, W.: Observer-based adaptive fuzzy fault-tolerant control for nonlinear systems using small-gain approach. Int. J. Fuzzy Syst. 21, 685–699 (2019). https://doi.org/10.1007/s40815-019-00607-z

    Article  MathSciNet  Google Scholar 

  22. Ge, Q., Jiang, H., He, M., et al.: Power load forecast based on fuzzy BP neural networks with dynamical estimation of weights. Int. J. Fuzzy Syst. 22, 956–969 (2020). https://doi.org/10.1007/s40815-019-00796-7

    Article  Google Scholar 

  23. Ha, S., Chen, L., Liu, H.: Adaptive fuzzy variable structure control of fractional-order nonlinear systems with input nonlinearities. Int. J. Fuzzy Syst. (2021). https://doi.org/10.1007/s40815-021-01105-x

    Article  Google Scholar 

  24. Wang, Y.Y., Li, S.Z., Wang, D., et al.: Adaptive time-delay control for cable-driven manipulators with enhanced nonsingular fast terminal sliding mode. IEEETrans. Ind. Electron. 68(3), 2356–2367 (2021). https://doi.org/10.1109/TIE.2020.2975473

    Article  Google Scholar 

  25. Wu, H.M., Karkoub, M.: Hierarchical fuzzy sliding-mode adaptive control for the trajectory tracking of differential-driven mobile robots. Int. J. Fuzzy Syst. 21, 33–49 (2019). https://doi.org/10.1007/s40815-018-0531-2

    Article  MathSciNet  Google Scholar 

  26. Falcón, R., Ríos, H., Dzul, A.: A sliding-mode-based active fault-tolerant control for robust trajectory tracking in quad-rotors under a rotor failure. Int. J. Robust Nonlinear Control. (2022). https://doi.org/10.1002/rnc.6288

  27. Chern, T.L., Wong, J.S.: DSP based integral variable structure control for DC motor servo drivers. IEE Proc. Control Theory Appl. 142(5), 444–450 (1995)

    Article  MATH  Google Scholar 

  28. Seshagiri, S., Khalil, H.K.: Robust output feedback regulation of minimum-phase nonlinear systems using conditional integrator. Automatica 41(1), 43–54 (2005). https://doi.org/10.1016/j.automatica.2004.08.013

    Article  MathSciNet  MATH  Google Scholar 

  29. Machado, J.T., Kiryakova, V., Mainardi, F.: Recent history of fractional calculus. Commun. Nonlinear Sci. Numer. Simul. 16(3), 1140–1153 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  30. Oustaloup, A., Moreau, X., Nouillant, M.: The CRONE suspension. Control Eng. Pract. 4(8), 1101–1108 (1996). https://doi.org/10.1016/S1474-6670(17)44677-5

    Article  Google Scholar 

  31. Podlubny, I.: Fractional Differential Equations: An Introduction to Fractional Derivatives, Fractional Differential Equations, to Methods of Their Solution and Some of Their Applications. Academic Press, New York (1998)

    MATH  Google Scholar 

  32. Yakoub, Z., Amairi, M., Chetoui, M., Saidi, B., Aoun, M.: Model-free adaptive fractional order control of stable linear time-varying systems. ISA Trans. 67, 193–207 (2017). https://doi.org/10.1016/j.isatra.2017.01.023

    Article  Google Scholar 

  33. Yang, Z.B., et al.: Fractional-order sliding mode control for a bearingless induction motor based on improved load torque observer. J. Franklin Inst. 358(7), 3701–3725 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  34. Wu, X.R., Huang, Y.Y.: Adaptive fractional-order non-singular terminal sliding mode control based on fuzzy wavelet neural networks for omnidirectional mobile robot manipulator. ISA Trans. (2021). https://doi.org/10.1016/j.isatra.2021.03.035

    Article  Google Scholar 

  35. Nikdel, N., Badamchizadeh, M., Azimirad, V.: Fractional-order adaptive backstepping control of robotic manipulators in the presence of model uncertainties and external disturbances. IEEE Trans. Ind. Electron. 63(10), 6249–6256 (2016). https://doi.org/10.1109/TIE.2016.2577624

  36. Wang, J., Shao, C., Chen, Y.Q.: Fractional order sliding mode control via disturbance observer for a class of fractional order systems with mismatched disturbance. Mechatronics 53, 8–19 (2018). https://doi.org/10.2139/ssrn.3281368

    Article  Google Scholar 

  37. Wang, Y.Y., Wu, H.T., Chen, B., et al.: A new continuous fractional-order nonsingular terminal sliding mode control for cable-driven manipulators. Adv. Eng. Softw. 119, 21–29 (2018). https://doi.org/10.1016/j.advengsoft.2018.01.011

    Article  Google Scholar 

  38. Albert, J.R., Stonier, A.A.: Design and development of symmetrical super-lift DC-AC converter using firefly algorithm for solar-photovoltaic applications. IET Circuit Devices Syst. 14, 261–269 (2020). https://doi.org/10.1049/iet-cds.2018.5292

    Article  Google Scholar 

  39. Hu, R., Deng, H., Zhang, Y.: Novel dynamic-sliding-mode-manifold-based continuous fractional-order nonsingular terminal sliding mode control for a class of second-order nonlinear systems. IEEE Access. 8, 19820–19829 (2020). https://doi.org/10.1109/ACCESS.2020.2968558

  40. Alipour, M., Malekzadeh, M., Ariaei, A.: Active fractional-order sliding mode control of flexible spacecraft under actuators saturation. J. Sound Vib. 535, 117110 (2022). https://doi.org/10.1016/j.jsv.2022.117110

    Article  Google Scholar 

  41. Liu, J.K.: Advanced PID Control and MATLAB Simulation. (2016)

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 51975294, in part by Open Fund of Aerospace Servo Drive and Transmission Technology Laboratory, No. LASAT-2021-0503, and was also supported by the Fundamental Research Funds for the Central Universities, No. 30922010706.

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  1. School of Mechanical Engineering, Nanjing University of Science and Technology, Xiaolingwei200#, Nanjing, 210094, China

    Yingzhe Sha, Jian Hu & Jianyong Yao

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  1. Yingzhe Sha
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Correspondence to Jian Hu.

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Sha, Y., Hu, J. & Yao, J. Active Fault-Tolerant Control Strategy for Electromechanical Servo System Based on Dual Fuzzy RBF Neural Networks and Velocity Reconstruction. Int. J. Fuzzy Syst. 25, 715–730 (2023). https://doi.org/10.1007/s40815-022-01398-6

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