Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Robust output feedback fault-tolerant control of non-linear multi-agent systems based on wavelet neural networks

  • Author(s): Maryam Salimifard 1  and  Heidar Ali Talebi 1, 2
    • View affiliations
  • Source: Volume 11, Issue 17, 24 November 2017, p. 3004 – 3015
    DOI:  10.1049/iet-cta.2016.1645 , Print ISSN 1751-8644, Online ISSN 1751-8652
© The Institution of Engineering and Technology
Received 13/01/2017, Accepted 21/08/2017, Revised 15/08/2017, Published 24/08/2017

A robust output feedback active fault-tolerant leader-following controller for a class of non-linear multi-agent systems is presented. It is assumed that the states of the followers are not available; therefore, a local observer is constructed to estimate the states of each agent. In addition, the non-linear dynamics of agents may include uncertainties and the control input of the leader dynamics is unknown to all followers. Moreover, taking advantage of wavelet neural networks (WNNs), an online fault estimation scheme is developed which can effectively approximate the unknown actuator faults. The proposed decentralised observer-based robust cooperative controller is capable of compensating for the effects of unknown time-varying additive actuator faults, the model uncertainties, and the unknown input of the leader simultaneously. The stability analysis and convergence results that guarantee boundedness of all closed-loop signals are investigated via Lyapunov's direct method. To demonstrate the effectiveness of the proposed approach, a network of single-link manipulators is studied. As the results verify, the proposed WNN-based fault estimation scheme can properly approximate the unknown actuator faults, which results in efficient compensation in the fault-tolerant control design to achieve cooperative tracking objectives.

Inspec keywords: Lyapunov methods; multi-agent systems; feedback; neurocontrollers; observers; fault tolerant control; actuators; robust control; closed loop systems; nonlinear control systems

Other keywords: local observer; fault-tolerant control design; closed-loop signals; time-varying additive actuator faults; leader dynamics; actuator faults; nonlinear multiagent systems; Lyapunov direct method; WNN; wavelet neural networks; cooperative tracking objectives; robust output feedback fault-tolerant control; stability analysis; nonlinear dynamics; online fault estimation scheme; fault tolerant leader following controller

Subjects: Stability in control theory; Simulation, modelling and identification; Nonlinear control systems; Neurocontrol; Artificial intelligence (theory)

References

    1. 1)
      • 17. Ye, D., Zhao, X., Cao, B.: ‘Distributed adaptive fault-tolerant consensus tracking of multi-agent systems against time-varying actuator faults’, IET Control Theory Appl., 2016, 10, (5), pp. 554–563.
    2. 2)
      • 22. Zhang, K., Jiang, B., Cocquempot, V.: ‘Adaptive technique-based distributed fault estimation observer design for multi-agent systems with directed graphs’, IET Control Theory Appl., 2015, 9, (18), pp. 2619–2625.
    3. 3)
      • 28. Khalili, M., Zhang, X., Polycarpou, M., et al: ‘Distributed adaptive fault-tolerant control of uncertain multi-agent systems’, IFAC-PapersOnLine, 2015, 48, (21), pp. 66–71.
    4. 4)
      • 33. Wen, G., Duan, Z., Li, Z., et al: ‘Stochastic consensus in directed networks of agents with non-linear dynamics and repairable actuator failures’, IET Control Theory Appl., 2012, 6, (11), pp. 1583–1593.
    5. 5)
      • 58. Yingwei, L., Sundararajan, N., Saratchandran, P.: ‘Identification of time-varying nonlinear systems using minimal radial basis function neural networks’, IEE Proc. Contr. Theor. Appl., 1997, 144, (2), pp. 202–208.
    6. 6)
      • 47. Wu, Q., Saif, M.: ‘Robust fault detection and diagnosis for a multiple satellite formation flying system using second order sliding mode and wavelet networks’. American Control Conf., New York, NY, USA, July 2007, pp. 426–431.
    7. 7)
      • 37. Ye, D., Chen, M., Li, K.: ‘Observer-based distributed adaptive fault-tolerant containment control of multi-agent systems with general linear dynamics’, ISA Trans., 2017, doi:10.1016/j.isatra.2017.06.007.
    8. 8)
      • 51. Lin, C.M., Hsueh, C.S., Chen, C.H.: ‘Robust adaptive backstepping control for a class of nonlinear systems using recurrent wavelet neural network’, Neurocomputing, 2014, 142, pp. 372–382.
    9. 9)
      • 49. Lin, C.M., Hsu, C.F.: ‘Neural-network hybrid control for antilock braking systems’, IEEE Trans. Neural Netw., 2003, 14, (2), pp. 351–359.
    10. 10)
      • 50. Peng, Y.F., Lin, C.M.: ‘Intelligent motion control of linear ultrasonic motor with H∞ tracking performance’, IET Control Theory Appl., 2007, 1, (1), pp. 9–17.
    11. 11)
      • 14. Chen, S., Ho, D.W., Li, L., et al: ‘Fault-tolerant consensus of multi-agent system with distributed adaptive protocol’, IEEE Trans. Cybern., 2015, 45, (10), pp. 2142–2155.
    12. 12)
      • 55. Xu, X., Gao, L.: ‘Intermittent observer-based consensus control for multi-agent systems with switching topologies’, Int. J. Syst. Sci., 2016, 47, (8), pp. 1891–1904.
    13. 13)
      • 45. Billings, S.A., Wei, H.L.: ‘A new class of wavelet networks for nonlinear system identification’, IEEE Trans. Neural Netw., 2005, 16, (4), pp. 862–874.
    14. 14)
      • 40. Rajamani, R.: ‘Observers for Lipschitz nonlinear systems’, IEEE Trans. Autom. Control, 1998, 43, (3), pp. 397–401.
    15. 15)
      • 10. Jiang, J., Yu, X.: ‘Fault-tolerant control systems: a comparative study between active and passive approaches’, Annu. Rev. Control, 2012, 36, (1), pp. 60–72.
    16. 16)
      • 5. Ren, W., Beard, R.W.: ‘Consensus seeking in multiagent systems under dynamically changing interaction topologies’, IEEE Trans. Autom. Control, 2005, 50, (5), pp. 655–661.
    17. 17)
      • 1. Ren, W., Beard, R.W., Atkins, E.M.: ‘Information consensus in multivehicle cooperative control’, IEEE Control Syst. Mag., 2007, 2, (27), pp. 71–82.
    18. 18)
      • 16. Wang, X., Yang, G.-H.: ‘Cooperative adaptive fault-tolerant tracking control for a class of multi-agent systems with actuator failures and mismatched parameter uncertainties’, IET Control Theory Appl., 2015, 9, (8), pp. 1274–1284.
    19. 19)
      • 38. Hong, Y., Hu, J., Gao, L.: ‘Tracking control for multi-agent consensus with an active leader and variable topology’, Automatica, 2006, 42, (7), pp. 1177–1182.
    20. 20)
      • 59. Diao, Y., Passino, K.M.: ‘Stable adaptive control of feedback linearizable time-varying non-linear systems with application to fault-tolerant engine control’, Int. J. Control, 2004, 77, (17), pp. 1463–1480.
    21. 21)
      • 18. Deng, C., Yang, G.-H.: ‘Distributed adaptive fault-tolerant containment control for a class of multi-agent systems with non-identical matching non-linear functions’, IET Control Theory Appl., 2016, 10, (3), pp. 273–281.
    22. 22)
      • 3. Lin, M., Ou, L.-L., Wang, M., et al: ‘The local control scheme for switching consensus value in multi-agent systems’, J. Control Decis., 2015, 2, (3), pp. 185–202.
    23. 23)
      • 35. Meskin, N., Khorasani, K.: ‘Actuator fault detection and isolation for a network of unmanned vehicles’, IEEE Trans. Autom. Control, 2009, 54, (4), pp. 835–840.
    24. 24)
      • 20. Liu, X., Gao, X., Han, J.: ‘Robust unknown input observer based fault detection for high-order multi-agent systems with disturbances’, ISA Trans., 2016, 61, pp. 15–28.
    25. 25)
      • 52. Corless, M., Tu, J.: ‘State and input estimation for a class of uncertain systems’, Automatica, 1998, 34, (6), pp. 757–764.
    26. 26)
      • 41. Lan, J., Patton, R.J.: ‘Integrated fault estimation and fault–tolerant control for uncertain Lipschitz nonlinear systems’, Int. J. Robust Nonlinear Control, 2017, 27, (5), pp. 761–780.
    27. 27)
      • 12. Azizi, S., Khorasani, K.: ‘A hierarchical architecture for cooperative actuator fault estimation and accommodation of formation flying satellites in deep space’, IEEE Trans. Aerosp. Electron. Syst., 2012, 48, (2), pp. 1428–1450.
    28. 28)
      • 24. Jin, X.: ‘Fault tolerant finite-time leader–follower formation control for autonomous surface vessels with LOS range and angle constraints’, Automatica, 2016, 68, pp. 228–236.
    29. 29)
      • 23. Zhang, K., Liu, G., Jiang, B.: ‘Robust unknown input observer-based fault estimation of leader–follower linear multi-agent systems’, Circuits Syst. Signal Process., 2017, 36, (2), pp. 525–542.
    30. 30)
      • 25. Yang, H., Staroswiecki, M., Jiang, B., et al: ‘Fault tolerant cooperative control for a class of nonlinear multi-agent systems’, Syst. Control Lett., 2011, 60, (4), pp. 271–277.
    31. 31)
      • 42. Jiang, B., Staroswiecki, M., Cocquempot, V.: ‘Fault accommodation for nonlinear dynamic systems’, IEEE Trans. Autom. Control, 2006, 51, (9), pp. 1578–1583.
    32. 32)
      • 61. Qiao, J., Zhang, Z., Bo, Y.: ‘An online self-adaptive modular neural network for time-varying systems’, Neurocomputing, 2014, 125, pp. 7–16.
    33. 33)
      • 56. Zhao, Y., Li, Z., Duan, Z.: ‘Distributed consensus tracking of multi-agent systems with nonlinear dynamics under a reference leader’, Int. J. Control, 2013, 86, (10), pp. 1859–1869.
    34. 34)
      • 11. Fekih, A.: ‘Fault-tolerant flight control design for effective and reliable aircraft systems’, J. Control Decis., 2014, 1, (4), pp. 299–316.
    35. 35)
      • 46. Hsu, C.F., Lin, C.M., Lee, T.T.: ‘Wavelet adaptive backstepping control for a class of nonlinear systems’, IEEE Trans. Neural Netw., 2006, 17, (5), pp. 1175–1183.
    36. 36)
      • 15. Chen, G., Song, Y.D.: ‘Robust fault-tolerant cooperative control of multi-agent systems: a constructive design method’, J. Franklin Inst., 2015, 352, (10), pp. 4045–4066.
    37. 37)
      • 2. Murray, R.M.: ‘Recent research in cooperative control of multivehicle systems’, J. Dyn. Syst. Meas. Control, 2007, 129, (5), pp. 571–583.
    38. 38)
      • 4. Rezaee, H., Abdollahi, F.: ‘Pursuit formation of double-integrator dynamics using consensus control approach’, IEEE Trans. Ind. Electron., 2015, 62, (7), pp. 4249–4256.
    39. 39)
      • 54. Wen, G., Duan, Z., Chen, G., et al: ‘Consensus tracking of multi-agent systems with Lipschitz-type node dynamics and switching topologies’, IEEE Trans. Circuits Syst. I, Reg. Papers, 2014, 61, (2), pp. 499–511.
    40. 40)
      • 32. Deng, C., Yang, G.-H.: ‘Cooperative adaptive output feedback control for nonlinear multi-agent systems with actuator failures’, Neurocomputing, 2016, 199, pp. 50–57.
    41. 41)
      • 27. Zuo, Z., Zhang, J., Wang, Y.: ‘Adaptive fault-tolerant tracking control for linear and Lipschitz nonlinear multi-agent systems’, IEEE Trans. Ind. Electron., 2015, 62, (6), pp. 3923–3931.
    42. 42)
      • 13. Zhou, B., Wang, W., Ye, H.: ‘Cooperative control for consensus of multi-agent systems with actuator faults’, Comput. Electr. Eng., 2014, 40, (7), pp. 2154–2166.
    43. 43)
      • 53. Li, Z., Liu, X., Fu, M., et al: ‘Global H∞ consensus of multi-agent systems with Lipschitz non-linear dynamics’, IET Control Theory Appl., 2012, 6, (13), pp. 2041–2048.
    44. 44)
      • 36. Zhao, D., Chi, M., Guan, Z.H., et al: ‘Distributed estimator-based fault detection for multi-agent networks’, Circuits Syst. Signal Process., 2017, pp. 1–14, doi:10.1007/s00034-017-0548-z.
    45. 45)
      • 26. Shen, Q., Jiang, B., Shi, P., et al: ‘Cooperative adaptive fuzzy tracking control for networked unknown nonlinear multi-agent systems with time-varying actuator faults’, IEEE Trans. Fuzzy Syst., 2014, 22, (3), pp. 494–504.
    46. 46)
      • 29. Wang, Y., Song, Y., Lewis, F.L.: ‘Robust adaptive fault-tolerant control of multiagent systems with uncertain nonidentical dynamics and undetectable actuation failures’, IEEE Trans. Ind. Electron., 2015, 62, (6), pp. 3978–3988.
    47. 47)
      • 60. Ahmed-Ali, T., Kenné, G., Lamnabhi-Lagarrigue, F.: ‘Identification of nonlinear systems with time-varying parameters using a sliding-neural network observer’, Neurocomputing, 2009, 72, (7), pp. 1611–1620.
    48. 48)
      • 44. Blanke, M., Kinnaert, M., Lunze, J., et al: ‘Diagnosis and fault-tolerant control’ (Springer, 2006, 2nd edn.).
    49. 49)
      • 6. Gu, G., Liu, F., Chen, X.: ‘Consensus control and feedback graph co-design for MIMO discrete-time multi-agent systems’, J. Control Decis., 2014, 1, (1), pp. 18–33.
    50. 50)
      • 30. Chen, G., Song, Y.: ‘Fault-tolerant output synchronisation control of multi-vehicle systems’, IET Control Theory Appl., 2014, 8, (8), pp. 574–584.
    51. 51)
      • 9. Zhang, Y., Jiang, J.: ‘Bibliographical review on reconfigurable fault-tolerant control systems’, Annu. Rev. Control, 2008, 32, (2), pp. 229–252.
    52. 52)
      • 34. Xie, C.H., Yang, G.-H.: ‘Cooperative guaranteed cost fault-tolerant control for multi-agent systems with time-varying actuator faults’, Neurocomputing, 2016, 214, pp. 382–390.
    53. 53)
      • 7. Semsar-Kazerooni, E., Khorasani, K.: ‘Team consensus for a network of unmanned vehicles in presence of actuator faults’, IEEE Trans. Control Syst. Technol., 2010, 18, (5), pp. 1155–1161.
    54. 54)
      • 21. Liu, X., Gao, X., Han, J.: ‘Observer-based fault detection for high-order nonlinear multi-agent systems’, J. Franklin Inst., 2016, 353, (1), pp. 72–94.
    55. 55)
      • 19. Shi, J., He, X., Wang, Z., et al: ‘Distributed fault detection for a class of second-order multi-agent systems: an optimal robust observer approach’, IET Control Theory Appl., 2014, 8, (12), pp. 1032–1044.
    56. 56)
      • 39. Polycarpou, M., Ioannou, P.: ‘A robust adaptive nonlinear control design’, Automatica, 1996, 32, (3), pp. 423–427.
    57. 57)
      • 62. Kiumarsi, B., Lewis, F.L., Levine, D.S.: ‘Optimal control of nonlinear discrete time-varying systems using a new neural network approximation structure’, Neurocomputing, 2015, 156, pp. 157–165.
    58. 58)
      • 43. Rajamani, R., Cho, Y.: ‘Existence and design of observers for nonlinear systems: relation to distance to unobservability’, Int. J. Control, 1998, 69, (5), pp. 717–731.
    59. 59)
      • 8. Meskin, N., Khorasani, K.: ‘Fault detection and isolation: multi-vehicle unmanned systems’ (Springer, 2011).
    60. 60)
      • 48. Lin, C.M., Boldbaatar, E.A.: ‘Fault accommodation control for a biped robot using a recurrent wavelet Elman neural network’, IEEE Syst. J., 2015, PP, (99), pp. 1–12.
    61. 61)
      • 31. Zhao, L., Jia, Y.: ‘Neural network-based adaptive consensus tracking control for multi-agent systems under actuator faults’, Int. J. Syst. Sci., 2016, 47, (8), pp. 1931–1942.
    62. 62)
      • 57. Li, Z., Ren, W., Liu, X., et al: ‘Consensus of multi-agent systems with general linear and Lipschitz nonlinear dynamics using distributed adaptive protocols’, IEEE Trans. Autom. Control, 2013, 58, (7), pp. 1786–1791.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cta.2016.1645
Loading

Related content

content/journals/10.1049/iet-cta.2016.1645
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address