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access icon free Fixed-time observer based adaptive neural network time-varying formation tracking control for multi-agent systems via minimal learning parameter approach

  • Author(s): Tianyi Xiong 1, 2, 3 ; Zhiqiang Pu 1, 3 ; Jianqiang Yi 1, 3 ; Xinlong Tao 1, 3
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  • Source: Volume 14, Issue 9, 11 June 2020, p. 1147 – 1157
    DOI:  10.1049/iet-cta.2019.0309 , Print ISSN 1751-8644, Online ISSN 1751-8652
© The Institution of Engineering and Technology
Received 22/03/2019, Accepted 05/03/2020, Revised 16/01/2020, Published 06/03/2020

This study proposes a novel control scheme to investigate the time-varying formation tracking control problem for multi-agent systems with model uncertainties and the absence of leader's velocity measurements. For each agent, a novel fixed-time cascaded leader state observer (CLSO) without velocity measurements is first designed to reconstruct the states of the leader. Radial basis function neural networks (RBFNNs) are adopted to deal with the model uncertainties online. Taking the square of the norm of the NN weight vector as a newly developed adaptive parameter, a novel RBFNN-based adaptive control scheme with minimal learning-parameter approach and fixed-time CLSO is then constructed to tackle the time-varying formation tracking problem. The uniform ultimate boundedness property of the formation tracking error is guaranteed through Lyapunov stability analysis. Finally, two simulation scenario results demonstrate the effectiveness of the proposed formation tracking control scheme.

Inspec keywords: radial basis function networks; robust control; learning (artificial intelligence); observers; uncertain systems; control system synthesis; multi-agent systems; position control; neurocontrollers; nonlinear control systems; time-varying systems; Lyapunov methods; closed loop systems; adaptive control

Other keywords: minimal learning-parameter approach; radial basis function neural networks; formation tracking control scheme; fixed-time observer-based adaptive neural network time-varying formation tracking control; model uncertainties; velocity measurements; fixed-time cascaded leader state observer; fixed-time CLSO; multiagent systems; time-varying formation tracking problem; RBFNN-based adaptive control scheme; time-varying formation tracking control problem

Subjects: Stability in control theory; Other topics in statistics; Simulation, modelling and identification; Time-varying control systems; Self-adjusting control systems; Control system analysis and synthesis methods; Learning in AI (theory); Nonlinear control systems; Neurocontrol; Spatial variables control

References

    1. 1)
      • 1. Dong, X., Zhou, Y., Ren, Z., et al: ‘Time-varying formation control for unmanned aerial vehicles with switching interaction topologies’, Control Eng. Pract., 2016, 46, pp. 26–36.
    2. 2)
      • 41. Cheng, L., Hou, Z., Tan, M., et al: ‘Neural-network-based adaptive leader-following control for multiagent systems with uncertainties’, IEEE Trans. Neural Netw., 2010, 21, (8), pp. 1351–1358.
    3. 3)
      • 34. Milad, S., Javad, A.: ‘Cooperative adaptive neural partial tracking errors constrained control for nonlinear multi-agent systems’, Int. J. Adapt. Control Signal Process., 2016, 30, (7), pp. 1019–1042.
    4. 4)
      • 32. Xu, B., Fan, Y., Zhang, S.: ‘Minimal-learning-parameter technique-based adaptive neural control of hypersonic flight dynamics without back-stepping’, Neurocomputing, 2015, 164, pp. 201–209.
    5. 5)
      • 44. Khalil, H.: ‘Nonlinear systems’ (Prentice Hall, NJ, 2002, 3rd edn.), pp. 168–174.
    6. 6)
      • 40. Zhao, L., Jia, Y.: ‘Neural network-based distributed adaptive attitude synchronization control of spacecraft formation under modified fast terminal sliding mode’, Neurocomputing, 2016, 171, pp. 230–241.
    7. 7)
      • 11. Xia, H., Huang, T., Shao, J., et al: ‘Leader-following formation control for second-order multi-agent systems with time-varying delays’, Trans. Inst. Meas. Control, 2014, 36, (5), pp. 627–636.
    8. 8)
      • 7. Xiong, T., Pu, Z., Yi, J.: ‘Time-varying formation finite-time tracking control for multi-UAV systems under jointly connected topologies’, Int. J. Intell. Comput. Cybern., 2017, 10, (4), pp. 478–490.
    9. 9)
      • 48. Qin, H., Wu, Z., Sun, Y., et al: ‘Fault-tolerant prescribed performance control algorithm for underwater acoustic sensor network nodes with thruster saturation’, IEEE Access, 2019, 7, pp. 69504–69515.
    10. 10)
      • 37. Tian, B., Lu, H., Zuo, Z., et al: ‘Fixed-time leader–follower output feedback consensus for second-order multiagent systems’, IEEE Trans. Cybern., 2019, 4, (49), pp. 1545–1550.
    11. 11)
      • 25. Zuo, Z., Tian, B., Defoort, M., et al: ‘Fixed-time consensus tracking for multiagent systems with high-order integrator dynamics’, IEEE Trans. Autom. Control, 2018, 63, (2), pp. 563–570.
    12. 12)
      • 30. Yu, J., Dong, X., Li, Q., et al: ‘Practical time-varying formation tracking for second-order non-linear multiagent systems with multiple leaders using adaptive neural networks’, IEEE Trans. Neural Netw. Learn. Syst., 2018, 29, (12), pp. 6015–6025.
    13. 13)
      • 19. Sun, T., Liu, F., Pei, H., et al: ‘Observer-based adaptive leader-following formation control for non-holonomic mobile robots’, IET Control Theory Appl., 2012, 6, (18), pp. 2835–2841.
    14. 14)
      • 6. Balch, T., Arkin, R.C.: ‘Behavior-based formation control for multi-robot teams’, IEEE Trans. Robot. Autom., 1998, 14, (6), pp. 926–939.
    15. 15)
      • 5. Lewis, M.A., Tan, K.: ‘High precision formation control of mobile robots using virtual structures’, Auton. Robots, 1997, 4, (4), pp. 387–403.
    16. 16)
      • 3. Millán, P., Orihuela, L., Jurado, I., et al: ‘Formation control of autonomous underwater vehicles subject to communication delays’, IEEE Trans. Control Syst. Technol., 2014, 22, (2), pp. 770–777.
    17. 17)
      • 23. Basin, M., Yu, P., Shtessel, Y.: ‘Finite- and fixed-time differentiators utilising HOSM techniques’, IET Control Theory Appl., 2017, 11, (8), pp. 1144–1152.
    18. 18)
      • 42. Zhang, H., Lewis, F.L.: ‘Adaptive cooperative tracking control of higher-order non-linear systems with unknown dynamics’, Automatica, 2012, 48, (7), pp. 1432–1439.
    19. 19)
      • 10. Ou, M., Do, H., Li, S.: ‘Finite-time formation control of multiple non-holonomic mobile robots’, Int. J. Robust Nonlinear Control, 2014, 24, (1), pp. 140–165.
    20. 20)
      • 49. Xue, R., Song, J., Cai, G.: ‘Distributed formation flight control of multi-UAV system with non-uniform time delays and jointly connected topologies’, Proc. Inst. Mech. Eng. G, J. Aerosp. Eng., 2016, 230, (10), pp. 1871–1881.
    21. 21)
      • 43. Zuo, Z.: ‘Non-singular fixed-time consensus tracking for second-order multi-agent networks’, Automatica, 2015, 54, pp. 305–309.
    22. 22)
      • 2. Wang, Y., Cheng, L., Hou, Z., et al: ‘Optimal formation of multirobot systems based on a recurrent neural network’, IEEE Trans. Neural Netw. Learn. Syst., 2016, 27, (2), pp. 322–333.
    23. 23)
      • 31. Chen, L., Li, C., Sun, Y., et al: ‘Distributed finite-time tracking control for multiple uncertain Euler-Lagrange systems with input saturations and error constraints’, IET Control Theory Appl., 2019, 13, (1), pp. 123–133.
    24. 24)
      • 27. Tu, Z., Yu, H., Xia, X.: ‘Decentralized finite-time adaptive consensus of multiagent systems with fixed and switching network topologies’, Neurocomputing, 2017, 219, pp. 59–67.
    25. 25)
      • 29. Liu, X., Ge, S.S., Goh, C.: ‘Neural-network-based switching formation tracking control of multiagents with uncertainties in constrained space’, IEEE Trans. Syst. Man Cybern. Syst., 2019, 49, (5), pp. 1006–1015.
    26. 26)
      • 13. Wang, R., Dong, X., Li, Q., et al: ‘Distributed time-varying formation control for multiagent systems with directed topology using an adaptive output-feedback approach’, IEEE Trans. Ind. Inf., 2019, 15, (8), pp. 4676–4685.
    27. 27)
      • 21. Wu, X., Wang, S., Xing, M.: ‘Observer-based leader-following formation control for multi-robot with obstacle avoidance’, IEEE Access, 2019, 7, pp. 14791–14798.
    28. 28)
      • 36. Zuo, Z., Han, Q., Ning, B., et al: ‘An explicit estimate for the upper bound of the settling time in fixed-time leader-following consensus of high-order multivariable multiagent systems’, IEEE Trans. Ind. Electron., 2019, 66, (8), pp. 6250–6259.
    29. 29)
      • 50. Han, J.: ‘From PID to active disturbance rejection control’, IEEE Trans. Ind. Electron., 2009, 56, (3), pp. 900–906.
    30. 30)
      • 39. Chen, G., Yue, Y., Song, Y.: ‘Finite-time cooperative-tracking control for networked Euler-Lagrange systems’, IET Control Theory Appl., 2013, 7, (11), pp. 1487–1497.
    31. 31)
      • 33. Bu, X., Wu, X., Huang, J., et al: ‘Minimal-learning-parameter based simplified adaptive neural back-stepping control of flexible air-breathing hypersonic vehicles without virtual controllers’, Neurocomputing, 2016, 175, pp. 816–825.
    32. 32)
      • 12. Yu, X., Liu, L.: ‘Distributed formation control of non-holonomic vehicles subject to velocity constraints’, IEEE Trans. Ind. Electron., 2016, 63, (2), pp. 1289–1298.
    33. 33)
      • 26. Yu, H., Xia, X.: ‘Adaptive consensus of multi-agents in networks with jointly connected topologies’, Automatica, 2012, 48, (8), pp. 1783–1790.
    34. 34)
      • 22. Zuo, Z., Han, Q., Ge, X., et al: ‘An overview of recent advances in fixed-time cooperative control of multiagent systems’, IEEE Trans. Ind. Inf., 2018, 14, (6), pp. 2322–2334.
    35. 35)
      • 24. Sun, J., Yi, J., Pu, Z., et al: ‘Fixed-time sliding-mode disturbance observer-based nonsmooth backstepping control for hypersonic vehicles’, IEEE Trans. Syst. Man Cybern. Syst., 2018, pp. 1–10, doi: 10.1109/TSMC. 2018.2847706, in press.
    36. 36)
      • 9. Xiao, F., Wang, L., Chen, J., et al: ‘Finite-time formation control for multi-agent systems’, Automatica, 2009, 45, (11), pp. 2605–2611.
    37. 37)
      • 17. Dong, X., Zhou, Y., Ren, Z., et al: ‘Time-varying formation tracking for second-order multi-agent systems subjected to switching topologies with application to quadrotor formation flying’, IEEE Trans. Ind. Electron., 2017, 64, (6), pp. 5014–5024.
    38. 38)
      • 45. Elhaki, O., Shojaei, K.: ‘Neural network-based target tracking control of underactuated autonomous underwater vehicles with a prescribed performance’, Ocean Eng., 2018, 167, pp. 239–256.
    39. 39)
      • 46. Shojaei, K., Yousefi, M.R.: ‘Tracking control of a convoy of autonomous robotic cars with a prescribed performance’, Trans. Inst. Meas. Control,2019, 41, (13), pp. 3725–3741.
    40. 40)
      • 28. Yu, H., Shen, Y., Xia, X.: ‘Adaptive finite-time consensus in multi-agent networks’, Syst. Control Lett., 2013, 62, (10), pp. 880–889.
    41. 41)
      • 14. Dong, X., Hu, G.: ‘Time-varying formation control for general linear multi-agent systems with switching directed topologies’, Automatica, 2016, 73, pp. 47–55.
    42. 42)
      • 35. Cao, Y., Ren, W., Meng, Z.: ‘Decentralized finite-time sliding-mode estimators and their applications in decentralized finite-time formation tracking’, Syst. Control Lett., 2010, 59, (9), pp. 522–529.
    43. 43)
      • 4. Li, X., Zhu, D.: ‘An adaptive SOM neural network method to distributed formation control of a group of AUVs’, IEEE Trans. Ind. Electron., 2018, 65, (10), pp. 8260–8270.
    44. 44)
      • 16. Ge, M., Guan, Z., Yang, C., et al: ‘Time-varying formation tracking of multiple manipulators via distributed finite-time control’, Neurocomputing, 2016, 202, pp. 20–26.
    45. 45)
      • 18. Liang, X., Wang, H., Liu, Y., et al: ‘Formation control of nonholonomic mobile robots without position and velocity measurements’, IEEE Trans. Robot., 2018, 34, (2), pp. 434–446.
    46. 46)
      • 20. Qin, W., Liu, Z., Chen, Z.: ‘A novel observer-based formation for non-linear multi-agent systems with time delay and intermittent communication’, Nonlinear Dyn., 2015, 79, (3), pp. 1651–1664.
    47. 47)
      • 8. Ren, W.: ‘Consensus strategies for cooperative control of vehicle formations’, IET Control Theory Appl., 2007, 1, (2), pp. 505–512.
    48. 48)
      • 47. Sun, Y., Dong, D., Qin, H., et al: ‘Distributed tracking control for multiple Euler-Lagrange systems with communication delays and input saturation’, ISA Trans., 2020, 96, pp. 245–254.
    49. 49)
      • 38. Ni, J., Liu, L., Liu, C., et al: ‘Fixed-time leader-following consensus for second-order multiagent systems with input delay’, IEEE Trans. Ind. Electron., 2017, 64, (11), pp. 8635–8646.
    50. 50)
      • 15. Wang, R., Dong, X., Li, Q., et al: ‘Distributed time-varying formation control for linear swarm systems with switching topologies using an adaptive output-feedback approach’, IEEE Trans. Syst. Man Cybern. Syst., 2019, 49, (12), pp. 2664–2675.
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