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Leader-follower consensus of nonlinear agricultural multiagents using distributed adaptive protocols

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Abstract

Agricultural multi-agent systems are expected to be fundamental to future intelligent agriculture and digital farming. This study deals with agricultural multirobots from the perspective of the control algorithm, and adaptive leader-following consensus protocol design problems are resolved for nonlinear multiagent systems. A fully distributed edge-based strategy adaptive law is discussed herein; thus, the multiagent consensus can be implemented without knowing global information. Unlike methodologies in existing literature on nonlinear consensus, the proposed methodology is considerably less conservative because of the incremental quadratic constraint containing a wider variety of nonlinearities. This means that, utilizing an incremental multiplier matrix with appropriate values, the control scheme can be applied to a broader class of nonlinear multi-agent systems, which is applicable to more agricultural fields. Finally, a numerical example consisting of six followers and one leader is provided to demonstrate the validity and effectiveness of the proposed protocol under a directed network.

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References

  1. Costa GB, Damiani JS, Marchesan G et al (2022) A multi-agent approach to distribution system fault section estimation in smart grid environment. Electr Power Syst Res 204:107658. https://doi.org/10.1016/j.epsr.2021.107658

    Article  Google Scholar 

  2. Miao Z, Yu J, Ji J et al (2019) Multi-objective region reaching control for a swarm of robots. Automatica 103:81–87

    Article  MathSciNet  MATH  Google Scholar 

  3. Xia W, Cao M, Johansson KH (2016) Structural balance and opinion separation in trust–mistrust social networks. IEEE Trans Control Netw Syst 3(1):46–56

    Article  MathSciNet  MATH  Google Scholar 

  4. Ju C, Kim J, Seol J et al (2022) A review on multirobot systems in agriculture. Comput Electron Agric 202:107336. https://doi.org/10.1016/j.compag.2022.107336

    Article  Google Scholar 

  5. Albiero D, Garcia AP, Umezu CK et al (2022) Swarm robots in mechanized agricultural operations: a review about challenges for research. Comput Electron Agric 193:106608. https://doi.org/10.1016/j.compag.2021.106608

    Article  Google Scholar 

  6. Su H, Zhang J, Chen X (2019) A stochastic sampling mechanism for time-varying formation of multiagent systems with multiple leaders and communication delays. IEEE Trans Neural Netw Learn Syst 30(12):3699–3707

    Article  MathSciNet  Google Scholar 

  7. Su H, Wu H, Chen X et al (2018) Positive edge consensus of complex networks. IEEE Trans Syst Man Cybern 48(12):2242–2250

    Article  Google Scholar 

  8. Qian Y, Zhang W, Ji M et al (2020) Observer-based positive edge consensus for directed nodal networks. IET Contr Theory Appl 14(2):352–357

    Article  MathSciNet  Google Scholar 

  9. Wu H, Zhu Z (2022) Improved results on distributed observer-based positive edge consensus. J Frankl Inst-Eng Appl Math. https://doi.org/10.1016/j.jfranklin.2022.05.016

    Article  Google Scholar 

  10. Zhao Y, Zhu F, Xu D (2022) Event-triggered bipartite time-varying formation control for multiagent systems with unknown inputs. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2022.3208228

    Article  Google Scholar 

  11. Zhang W, Su H, Zhu F et al (2013) Observer-based H∞ synchronization and unknown input recovery for a class of digital nonlinear systems. Circ Syst Signal Process 32(6):2867–2881

    Article  MathSciNet  Google Scholar 

  12. Wang Z, Xun Y, Wang Y et al (2022) Review of smart robots for fruit and vegetable picking in agriculture. Int J Agric Biol Eng 15(1):33–54

    MathSciNet  Google Scholar 

  13. Zhang C, Noguchi N (2017) Development of a multi-robot tractor system for agriculture field work. Comput Electron Agric 142:79–90

    Article  Google Scholar 

  14. Li Z, Duan Z, Chen G et al (2010) Consensus of multiagent systems and synchronization of complex networks: a unified viewpoint. IEEE Trans Circuits Syst I-Regul Pap 57(1):213–224

    Article  MathSciNet  MATH  Google Scholar 

  15. Li Z, Ren W, Liu X et al (2013) Consensus of multi-agent systems with general linear and Lipschitz nonlinear dynamics using distributed adaptive protocols. IEEE Trans Autom Control 58(7):1786–1791

    Article  MathSciNet  MATH  Google Scholar 

  16. Yan C, Zhang W, Su H et al (2022) Adaptive bipartite time-varying output formation control for multiagent systems on signed directed graphs. IEEE Trans Cybern 52(9):8987–9000

    Article  Google Scholar 

  17. Noguchi N, Will J, Reid J et al (2004) Development of a master–slave robot system for farm operations. Comput Electron Agric 44:1–19

    Article  Google Scholar 

  18. Johnson DA, Naffin DJ, Puhalla JS et al (2009) Development and implementation of a team of robotic tractors for autonomous peat moss harvesting. J Field Robot 26(6/7):549–571

    Article  Google Scholar 

  19. Zhang W, Su H, Zhu F et al (2012) A note on observers for discrete-time Lipschitz nonlinear systems. IEEE Trans Circuits Syst II-Express Briefs 59(2):123–127

    Article  Google Scholar 

  20. Zou W, Shi P, Xiang Z et al (2020) Finite-time consensus of second-order switched nonlinear multi-agent systems. IEEE Trans Syst Man Cybern 31(5):1757–1762

    MathSciNet  Google Scholar 

  21. Zou W, Guo J, Ahn CK et al (2022) Sampled-data consensus protocols for a class of second-order switched nonlinear multiagent systems. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2022.3163157

    Article  Google Scholar 

  22. Zou W, Zhou C, Guo J et al (2021) Global adaptive leader-following consensus for second-order nonlinear multiagent systems with switching topologies. IEEE Trans Circuits Syst II-Express Briefs 68(2):702–706

    Google Scholar 

  23. Abbaszadeh M, Marquez HJ (2010) Nonlinear observer design for one-sided Lipschitz systems. In: Proceedings of the 2010 American control conference, June 30–July 2, 2010, Baltimore, MD, USA, pp 5284‒5289

  24. Zhang W, Su H, Liang Y et al (2012) Non-linear observer design for one-sided Lipschitz systems: an linear matrix inequality approach. IET Control Theory Appl 6(9):1297–1303

    Article  MathSciNet  Google Scholar 

  25. Cao Y, Zhang L, Li C et al (2017) Observer-based consensus tracking of nonlinear agents in hybrid varying directed topology. IEEE Trans Cybern 47(8):2212–2222

    Article  Google Scholar 

  26. Açıkmeşe B, Corless M (2011) Observers for systems with nonlinearities satisfying incremental quadratic constraints. Automatica 47:1339–1348

    Article  MathSciNet  MATH  Google Scholar 

  27. Zhao Y, Zhang W, Su H et al (2020) Observer-based synchronization of chaotic systems satisfying incremental quadratic constraints and its application in secure communication. IEEE Trans Syst Man Cybern 50(12):5221–5232

    Article  Google Scholar 

  28. Wang X, Wang X, Su H et al (2022) Reduced-order interval observer based consensus for MASs with time-varying interval uncertainties. Automatica 135:109989. https://doi.org/10.1016/j.automatica.2021.109989

    Article  MathSciNet  MATH  Google Scholar 

  29. Xu X, Açıkmeşe B, Corless MJ (2021) Observer-based controllers for incrementally quadratic nonlinear systems with disturbances. IEEE Trans Autom Control 66(3):1129–1143

    Article  MathSciNet  MATH  Google Scholar 

  30. Li X, Liu F, Buss M et al (2020) Fully distributed consensus control for linear multiagent systems: a reduced-order adaptive feedback approach. IEEE Trans Control Netw Syst 7(2):967–976

    Article  MathSciNet  MATH  Google Scholar 

  31. Chu H, Liu X, Zhang W et al (2016) Observer-based consensus tracking of multi-agent systems with one-sided Lipschitz nonlinearity. J Frankl Inst-Eng Appl Math 353:1594–1614

    Article  MathSciNet  MATH  Google Scholar 

  32. Yan C, Zhang W, Li X et al (2020) Observer-based time-varying formation tracking for one-sided Lipschitz nonlinear systems via adaptive protocol. Int J Control Autom Syst 18:2753–2764

    Article  Google Scholar 

  33. Li S, Ahn CK, Guo J et al (2021) Neural network-based sampled-data control for switched uncertain nonlinear systems. IEEE Trans Syst Man Cybern 51(9):5437–5445

    Article  Google Scholar 

  34. Yu Y, Guo J, Ahn CK et al (2022) Neural adaptive distributed formation control of nonlinear multi-UAVs with unmodeled dynamics. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2022.3157079

    Article  Google Scholar 

  35. Zhang Y, Guo J, Xiang Z (2022) Finite-time adaptive neural control for a class of nonlinear systems with asymmetric time-varying full-state constraints. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2022.3164948

    Article  Google Scholar 

  36. Yu J, Dong X, Li Q et al (2018) Practical time-varying formation tracking for second-order nonlinear multiagent systems with multiple leaders using adaptive neural networks. IEEE Trans Neural Netw Learn Syst 29(12):6015–6025

    Article  MathSciNet  Google Scholar 

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Funding

Funding was provided by National Natural Science Foundation of China-China Academy of General Technology Joint Fund for Basic Research (Grant No. 51875331).

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Correspondence to Zhong-Hua Miao.

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Qian, YC., Miao, ZH., Zhou, J. et al. Leader-follower consensus of nonlinear agricultural multiagents using distributed adaptive protocols. Adv. Manuf. (2023). https://doi.org/10.1007/s40436-023-00449-x

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  • DOI: https://doi.org/10.1007/s40436-023-00449-x

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