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Adaptive estimation-based TILC for the finite-time consensus control of non-linear discrete-time MASs under directed graph

Adaptive estimation-based TILC for the finite-time consensus control of non-linear discrete-time MASs under directed graph

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This work explores the consensus problems under the directed graph, variable learning gains, fast convergence and data-driven control framework comprehensively and proposes an adaptive estimation-based terminal iterative learning control for a nonlinear discrete-time multi-agent system (MAS) with a constant control input. A linear iteration-incremental model is built by using an iterative dynamic linearisation where the unknown partial derivatives are estimated iteratively using I/O data. The learning control law is designed with both a constant learning gain and an iteration-time-varying learning gain. The constant one can be selected properly according to the estimation of partial derivatives and the varying one can be estimated from iteratively utilising I/O data. The result has also been extended to the nonlinear MAS with time-varying control input and an extended adaptive estimation-based TILC is developed by using time-varying control input to enhance the control performance. A fast convergence of both the proposed methods is achieved by removing the unnecessary error constraints at other time instants than the endpoint. Both the proposed methods is apparently data-driven since no model information is involved. The proposed finite time consensus control methods are confirmed to be effective under the directed graph through mathematic proof and extensive simulations.

References

    1. 1)
      • 1. Zhang, H.T., Chen, Z., Yan, L.: ‘Applications of collective circular motion control to multi-robot systems’, IEEE Trans. Control Syst. Technol., 2013, 21, (4), pp. 14161422.
    2. 2)
      • 2. Fax, J.A., Murray, R.M.: ‘Information flow and cooperative control of vehicle formations’, IEEE Trans. Autom. Control, 2004, 49, (9), pp. 14651476.
    3. 3)
      • 3. Kang, W., Yeh, H.H.: ‘Co-ordinated attitude control of multi-satellite systems’, Int. J. Robust Nonlinear Control, 2002, 12, (2), pp. 185205.
    4. 4)
      • 4. Cao, Y.C., Ren, W.: ‘Finite-time consensus for multi-agent networks with unknown inherent nonlinear dynamics’, Automatica, 2014, 50, pp. 26482656.
    5. 5)
      • 5. Dimarogonas, D., Frazzoli, E., Johansson, K.: ‘Distributed event-triggered control for multi-agent systems’, IEEE Trans. Autom. Control, 2012, 57, pp. 12911297.
    6. 6)
      • 6. Hu, W., Liu, L., Feng, G.: ‘Consensus of linear multi-agent systems by distributed event-triggered strategy’, IEEE Trans. Cybern., 2016, 46, pp. 148157.
    7. 7)
      • 7. He, X., Wang, Q.: ‘Distributed finite-time leaderless consensus control for double-integrator multi-agent systems with external disturbances’, Appl. Math. Comput., 2017, 295, pp. 6567.
    8. 8)
      • 8. Cao, Z., Li, C., Wang, X., et al: ‘Finite-time consensus of linear multi-agent system via distributed event-triggered strategy’, J. Franklin Inst., 2018, 355, pp. 13381350.
    9. 9)
      • 9. Zhang, H., Yue, D., Yin, X., et al: ‘Finite-time distributed event-triggered consensus control for multi-agent systems’, Inf. Sci., 2016, 339, pp. 132142.
    10. 10)
      • 10. Ahn, H.S., Moore, K.L., Chen, Y.: ‘Trajectory-keeping in satellite formation flying via robust periodic learning control’, Int. J. Robust Nonlinear Control, 2010, 20, (14), pp. 16551666.
    11. 11)
      • 11. Yufka, A., Parlaktuna, O., Ozkan, M.: ‘Formation-based cooperative transportation by a group of non-holonomic mobile robots’, IEEE Int. Conf. Syst. Man Cybern., 2010, 12, (4), pp. 33003307.
    12. 12)
      • 12. Arimoto, S., Kawamura, S., Miyazaki, F.: ‘Bettering operation of robots by learning’, J. Robot Syst., 1984, 1, (2), pp. 123140.
    13. 13)
      • 13. Chi, R.H., Hou, Z.S.: ‘Model-free periodic adaptive control for a class of SISO nonlinear discrete-time systems’. 8th IEEE Int. Conf. on Control and Automation, Xiamen, China, June 2010, pp. 911.
    14. 14)
      • 14. Ahn, H.S., Chen, Y.Q.: ‘Iterative learning control for multi-agent formation’. ICROS-SICE Int. Joint Conf., Japan, August 2009, pp. 1821.
    15. 15)
      • 15. Liu, Y., Jia, Y.: ‘An iterative learning approach to formation control of multi-agent systems’, Syst. Control Lett., 2012, 61, pp. 148154.
    16. 16)
      • 16. Yang, S., Xu, J.X., Huang, D.Q., et al: ‘Optimal iterative learning control design for multi-agent systems consensus tracking’, Syst. Control Lett., 2014, 69, pp. 8089.
    17. 17)
      • 17. Yang, S., Xu, J.X., Li, X.: ‘Iterative learning control with input sharing for multi-agent consensus tracking’, Syst. Control Lett., 2016, 94, pp. 97106.
    18. 18)
      • 18. Meng, D., Jia, Y., Du, J., et al: ‘On iterative learning algorithms for the formation control of nonlinear multi-agent systems’, Automatica, 2014, 50, pp. 291295.
    19. 19)
      • 19. Li, J.M., Li, J.S.: ‘Adaptive fuzzy iterative learning control with initial-state learning for coordination control of leader-following multi-agent systems’, Fuzzy Sets Syst., 2014, 248, pp. 122137.
    20. 20)
      • 20. Li, J.M., Li, J.S.: ‘Coordination control of multi-agent systems with second-order nonlinear dynamics using fully distributed adaptive iterative learning’, J. Franklin Inst., 2015, 352, pp. 24412463.
    21. 21)
      • 21. Jin, X.: ‘Adaptive iterative learning control for high-order nonlinear multi-agent systems consensus tracking’, Syst. Control Lett., 2016, 89, pp. 1623.
    22. 22)
      • 22. Maupong, T.M., Rapisarda, P.: ‘Data-driven control: a behavioral approach’, Syst. Control Lett., 2017, 101, pp. 3743.
    23. 23)
      • 23. Chi, R.H., Hou, Z.S., Huang, B.: ‘Optimal iterative learning control of batch processes: from model-based to data-driven’, Acta Autom. Control, 2017, 43, (6), pp. 917932.
    24. 24)
      • 24. Tanaskovic, M., Fagiano, L., Novara, C., et al: ‘Data-driven control of nonlinear systems: an on-line direct approach’, Automatica, 2017, 75, (1), pp. 110.
    25. 25)
      • 25. Hou, Z.S., Chi, R.H., Gao, H.: ‘An overview of dynamic linearization based data-driven control and applications’, IEEE Trans. Ind. Electron., 2017, 64, (5), pp. 40764090.
    26. 26)
      • 26. Xu, J.X., Chen, Y., Lee, T.H., et al: ‘Terminal iterative learning control with an application to RTPCVD thickness control’, Automatica, 1999, 35, pp. 15351542.
    27. 27)
      • 27. Han, J., Shen, D., Chien, C.J.: ‘Terminal iterative learning control for discrete-time nonlinear systems based on neural networks’, J. Franklin Inst., 2018, 355, pp. 36413658.
    28. 28)
      • 28. Chi, R.H., Wang, D.W., Hou, Z.S., et al: ‘Data-driven optimal terminal iterative learning control’, J. Process Control, 2012, 22, pp. 20262037.
    29. 29)
      • 29. Chi, R.H., Hou, Z.S., Huang, B., et al: ‘A unified data-driven design framework of optimality-based generalized iterative learning control’, Comput. Chem. Eng., 2015, 77, pp. 1023.
    30. 30)
      • 30. Chi, R.H., Lin, N., Zhang, R.K., et al: ‘Stochastic high-order internal model-based adaptive TILC with random uncertainties in initial states and desired reference points’, Int. J. Adapt. Control Signal Process., 2017, 31, (5), pp. 726741.
    31. 31)
      • 31. Chi, R.H., Hou, Z.S., Jin, S.T., et al: ‘Enhanced data-driven optimal terminal ILC using current iteration control knowledge’, IEEE Trans. Neural Netw. Learn. Syst., 2015, 26, (11), pp. 29392948.
    32. 32)
      • 32. Chi, R.H., Liu, Y., Hou, Z.S., et al: ‘Data-driven terminal iterative learning control with high-order learning law for a class of non-linear discrete-time multiple-input–multiple output systems’, IET Control Theory Appl., 2015, 9, (7), pp. 10751082.
    33. 33)
      • 33. Chi, R.H., Huang, B., Wang, D.W., et al: ‘Data-driven optimal terminal iterative learning control with initial value dynamic compensation’, IET Control Theory Appl., 2016, 10, (12), pp. 13571364.
    34. 34)
      • 34. Chi, R.H., Hou, Z.S., Jin, S.T., et al: ‘Improved data-driven optimal TILC using time-varying input signals’, J. Process Control, 2014, 24, pp. 7885.
    35. 35)
      • 35. Meng, D., Jia, Y.: ‘Iterative learning approaches to design finite-time consensus protocols for multi-agent systems’, Syst. Control Lett., 2012, 61, pp. 187194.
    36. 36)
      • 36. Sun, M., Wang, D.: ‘Closed-loop iterative learning control for non-linear systems with initial shifts’, Int. J. Adapt. Control Signal Process., 2002, 16, (7), pp. 515538.
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