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Cooperative neural-adaptive fault-tolerant output regulation for heterogeneous nonlinear uncertain multiagent systems with disturbance

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Abstract

This paper investigates the cooperative output regulation problem for heterogeneous nonlinear uncertain multiagent networked systems subject to actuator failure, bounded matched or mismatched disturbances or disturbances produced by a given linear exosystem. Accurate information about nonlinearity, actuator failure and disturbance may be completely unknown. First, a distributed finite-time observer is designed to estimate the dynamics of the exosystem on a finite-time interval over a communication digraph. Then, a neural-adaptive control protocol is proposed. It is shown that (i) closed-loop multiagent systems are asymptotically stable, with output synchronization errors that tend to zero in the absence of mismatched disturbance, and (ii) the states of the closed-loop multiagent systems and the output synchronization errors are bounded in the presence of mismatched disturbance. Finally, a simulation example is given to demonstrate the effectiveness of the proposed control strategy.

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References

  1. Ren W, Beard R W. Distributed Consensus in Multi-vehicle Cooperative Control. London: Springer, 2008

    Book  MATH  Google Scholar 

  2. Lin Z L. Control design in the presence of actuator saturation: from individual systems to multi-agent systems. Sci China Inf Sci, 2019, 62: 026201

    Article  Google Scholar 

  3. Tian L, Ji Z J, Hou T, et al. Bipartite consensus of edge dynamics on coopetition multi-agent systems. Sci China Inf Sci, 2019, 62: 229201

    Article  MathSciNet  Google Scholar 

  4. Wen G X, Chen C L P, Dou H, et al. Formation control with obstacle avoidance of second-order multi-agent systems under directed communication topology. Sci China Inf Sci, 2019, 62: 192205

    Article  MathSciNet  Google Scholar 

  5. Rehan M, Ahn C K, Chadli M. Consensus of one-sided Lipschitz multi-agents under input saturation. IEEE Trans Circ Syst II, 2020, 67: 745–749

    Google Scholar 

  6. Rehman A U, Rehan M, Iqbal N, et al. Toward the LPV approach for adaptive distributed consensus of Lipschitz multi-agents. IEEE Trans Circ Syst II, 2019, 66: 91–95

    Google Scholar 

  7. Xiao W B, Cao L, Li H Y, et al. Observer-based adaptive consensus control for nonlinear multi-agent systems with time-delay. Sci China Inf Sci, 2020, 63: 132202

    Article  MathSciNet  Google Scholar 

  8. Francis B A, Wonham W M. The internal model principle of control theory. Automatica, 1976, 12: 457–465

    Article  MathSciNet  MATH  Google Scholar 

  9. Chen J Y, Li Z H, Ding Z T. Adaptive output regulation of uncertain nonlinear systems with unknown control directions. Sci China Inf Sci, 2019, 62: 089205

    Article  MathSciNet  Google Scholar 

  10. Su Y F, Huang J. Cooperative output regulation of linear multi-agent systems. IEEE Trans Automat Contr, 2012, 57: 1062–1066

    Article  MathSciNet  MATH  Google Scholar 

  11. Zhang Y, Su Y F. Cooperative output regulation for linear uncertain MIMO multi-agent systems by output feedback. Sci China Inf Sci, 2018, 61: 092206

    Article  MathSciNet  Google Scholar 

  12. Xu Y, Fang M, Pan Y J, et al. Event-triggered output synchronization for nonhomogeneous agent systems with periodic denial-of-service attacks. Int J Robust Nonlinear Control, 2020. doi: https://doi.org/10.1002/rnc.5223

  13. Dong S L, Ren W, Wu Z G, et al. H output consensus for Markov jump multiagent systems with uncertainties. IEEE Trans Cybern, 2020, 50: 2264–2273

    Article  Google Scholar 

  14. Li H Y, Shi P, Yao D Y. Adaptive sliding mode control of Markov jump nonlinear systems with actuator faults. IEEE Trans Autom Control, 2017, 62: 1933–1939

    Article  MathSciNet  MATH  Google Scholar 

  15. Li H Y, Wu Y, Chen M. Adaptive fault-tolerant tracking control for discrete-time multiagent systems via reinforcement learning algorithm. IEEE Trans Cybern, 2021, 51: 1163–1174

    Article  Google Scholar 

  16. Dong S L, Liu M Q, Wu Z G, et al. Observer-based sliding mode control for Markov jump systems with actuator failures and asynchronous modes. IEEE Trans Circ Syst II-Express Briefs, 2020. doi: https://doi.org/10.1109/TCSII.2020.3030703

  17. Zhang X D, Parisini T, Polycarpou M M. Adaptive fault-tolerant control of nonlinear uncertain systems: an information-based diagnostic approach. IEEE Trans Automat Contr, 2004, 49: 1259–1274

    Article  MathSciNet  MATH  Google Scholar 

  18. Li Y X. Finite time command filtered adaptive fault tolerant control for a class of uncertain nonlinear systems. Automatica, 2019, 106: 117–123

    Article  MathSciNet  MATH  Google Scholar 

  19. Wang W, Wen C Y. Adaptive actuator failure compensation control of uncertain nonlinear systems with guaranteed transient performance. Automatica, 2010, 46: 2082–2091

    Article  MathSciNet  MATH  Google Scholar 

  20. Ma H J, Yang G H. Adaptive fault tolerant control of cooperative heterogeneous systems with actuator faults and unreliable interconnections. IEEE Trans Automat Contr, 2016, 61: 3240–3255

    Article  MathSciNet  MATH  Google Scholar 

  21. Kim Y H, Lewis F L. Optimal design of CMAC neural-network controller for robot manipulators. IEEE Trans Syst Man Cybernet Part C (Appl Rev), 2000, 30: 22–31

    Article  Google Scholar 

  22. He W, Dong Y T, Sun C Y. Adaptive neural impedance control of a robotic manipulator with input saturation. IEEE Trans Syst Man Cybernet Syst, 2016, 46: 334–344

    Article  Google Scholar 

  23. Wang H, Yu W W, Ding Z T, et al. Tracking consensus of general nonlinear multiagent systems with external disturbances under directed networks. IEEE Trans Automat Contr, 2019, 64: 4772–4779

    Article  MathSciNet  MATH  Google Scholar 

  24. Wen G H, Wang P J, Huang T W, et al. Robust neuro-adaptive containment of multileader multiagent systems with uncertain dynamics. IEEE Trans Syst Man Cybernet Syst, 2019, 49: 406–417

    Article  Google Scholar 

  25. Peng Z H, Wang D, Zhang H W, et al. Distributed neural network control for adaptive synchronization of uncertain dynamical multiagent systems. IEEE Trans Neural Netw Learn Syst, 2014, 25: 1508–1519

    Article  Google Scholar 

  26. Deng C, Yang G H. Distributed adaptive fault-tolerant control approach to cooperative output regulation for linear multi-agent systems. Automatica, 2019, 103: 62–68

    Article  MathSciNet  MATH  Google Scholar 

  27. Liu M, Ho D W C, Shi P. Adaptive fault-tolerant compensation control for Markovian jump systems with mismatched external disturbance. Automatica, 2015, 58: 5–14

    Article  MathSciNet  MATH  Google Scholar 

  28. Zuo Z Q, Ho D W C, Wang Y J. Fault tolerant control for singular systems with actuator saturation and nonlinear perturbation. Automatica, 2010, 46: 569–576

    Article  MathSciNet  MATH  Google Scholar 

  29. Huang J. Nonlinear Output Regulation: Theory and Application. Philadelphia: Society for Industrial and Applied Mathematics, 2004

    Book  MATH  Google Scholar 

  30. Shevitz D, Paden B. Lyapunov stability theory of nonsmooth systems. IEEE Trans Autom Control, 1994, 39: 1910–1914

    Article  MathSciNet  MATH  Google Scholar 

  31. Bacciotti A, Ceragioli F. Stability and stabilization of discontinuous systems and nonsmooth Lyapunov functions. ESAIM Control Optim Calc, 1999, 4: 361–376

    Article  MathSciNet  MATH  Google Scholar 

  32. Cortés J, Bullo F. Coordination and geometric optimization via distributed dynamical systems. SIAM J Control Optim, 2005, 44: 1543–1574

    Article  MathSciNet  MATH  Google Scholar 

  33. Cao Y, Ren W, Casbeer D W, et al. Finite-time connectivity-preserving consensus of networked nonlinear agents with unknown Lipschitz terms. IEEE Trans Automat Contr, 2016, 61: 1700–1705

    Article  MathSciNet  MATH  Google Scholar 

  34. Franceschelli M, Pisano A, Giua A, et al. Finite-time consensus with disturbance rejection by discontinuous local interactions in directed graphs. IEEE Trans Automat Contr, 2015, 60: 1133–1138

    Article  MathSciNet  MATH  Google Scholar 

  35. Sarangapani J. Neural Network Control of Nonlinear Discrete-Time Systems. Boca Raton: CRC Press, 2006

    Book  Google Scholar 

  36. Lewis F L, Dawson D M, Abdallah C T. Robot Manipulator Control: Theory and Practice. Boca Raton: CRC Press, 2003

    Book  Google Scholar 

  37. Yucelen T, Haddad W M. Low-frequency learning and fast adaptation in model reference adaptive control. IEEE Trans Automat Contr, 2013, 58: 1080–1085

    Article  MathSciNet  MATH  Google Scholar 

  38. Chen Z Y, Huang J. Stabilization and Regulation of Nonlinear Systems: A Robust and Adaptive Approach. Berlin: Springer, 2015

    Book  MATH  Google Scholar 

  39. Lu Z H, Zhang L, Wang L. Controllability analysis of multi-agent systems with switching topology over finite fields. Sci China Inf Sci, 2019, 62: 012201

    Article  MathSciNet  Google Scholar 

  40. Liu J W, Huang J. Leader-following consensus of linear discrete-time multi-agent systems subject to jointly connected switching networks. Sci China Inf Sci, 2018, 61: 112208

    Article  MathSciNet  Google Scholar 

  41. Guan Y Q, Wang L. Structural controllability of multi-agent systems with absolute protocol under fixed and switching topologies. Sci China Inf Sci, 2017, 60: 092203

    Article  MathSciNet  Google Scholar 

  42. Chen Z Y, Han Q-L, Wu Z-G, et al. Special focus on advanced techniques for event-triggered control and estimation. Sci China Inf Sci, 2020, 63: 150200

    Article  Google Scholar 

  43. Chen Z Y, Han Q-L, Yan Y M, et al. How often should one update control and estimation: review of networked triggering techniques. Sci China Inf Sci, 2020, 63: 150201

    Article  MathSciNet  Google Scholar 

  44. Ma H, Li H Y, Lu R Q, et al. Adaptive event-triggered control for a class of nonlinear systems with periodic disturbances. Sci China Inf Sci, 2020, 63: 150212

    Article  MathSciNet  Google Scholar 

  45. Wang Q, Wu Z G. Robust output feedback control for input-saturated systems based on a sliding mode observer. Circ Syst Signal Process, 2021, 40: 2267–2281

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by National Key R&D Program of China (Grant No. 2018YFB1702802), Science Fund for Creative Research Groups of the National Natural Science Foundation of China (Grant No. 61621002), NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization (Grant No. U1709203), Zhejiang Provincial Natural Science Foundation of China (Grant No. LZ19F030002), and Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China (Grant No. ICT20068).

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Correspondence to Shanling Dong.

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Dong, S., Chen, G., Liu, M. et al. Cooperative neural-adaptive fault-tolerant output regulation for heterogeneous nonlinear uncertain multiagent systems with disturbance. Sci. China Inf. Sci. 64, 172212 (2021). https://doi.org/10.1007/s11432-020-3122-6

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  • DOI: https://doi.org/10.1007/s11432-020-3122-6

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