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

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.

Keywords

Multiagent consensus control / Distributed adaptive protocols / Incrementally quadratic nonlinearity / Observer-based protocol / Agriculture robots

References

[1] Costa GB, Damiani JS, Marchesan G, et al. A multi-agent approach to distribution system fault section estimation in smart grid environment. Electr Power Syst Res, 2022, 204: 107658.

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

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

[4] Ju C, Kim J, Seol J, et al. A review on multirobot systems in agriculture. Comput Electron Agric, 2022, 202: 107336.

[5] Albiero D, Garcia AP, Umezu CK, et al. Swarm robots in mechanized agricultural operations: a review about challenges for research. Comput Electron Agric, 2022, 193: 106608.

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

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

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

[9] Wu H, Zhu Z. Improved results on distributed observer-based positive edge consensus. J Frankl Inst-Eng Appl Math, 2022.

[10] Zhao Y, Zhu F, Xu D. Event-triggered bipartite time-varying formation control for multiagent systems with unknown inputs. IEEE Trans Cybern, 2022.

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

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

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

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

[15] 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): 1786-1791.

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

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

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

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

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

[21] Zou W, Guo J, Ahn CK, et al. Sampled-data consensus protocols for a class of second-order switched nonlinear multiagent systems. IEEE Trans Cybern, 2022.

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

[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. Non-linear observer design for one-sided Lipschitz systems: an linear matrix inequality approach. IET Control Theory Appl, 2012, 6(9): 1297-1303.

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

[26] Açıkmeşe B, Corless M. Observers for systems with nonlinearities satisfying incremental quadratic constraints. Automatica, 2011, 47: 1339-1348.

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

[28] Wang X, Wang X, Su H, et al. Reduced-order interval observer based consensus for MASs with time-varying interval uncertainties. Automatica, 2022, 135: 109989.

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

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

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

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

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

[34] Yu Y, Guo J, Ahn CK, et al. Neural adaptive distributed formation control of nonlinear multi-UAVs with unmodeled dynamics. IEEE Trans Neural Netw Learn Syst, 2022.

[35] Zhang Y, Guo J, Xiang Z. Finite-time adaptive neural control for a class of nonlinear systems with asymmetric time-varying full-state constraints. IEEE Trans Neural Netw Learn Syst, 2022.

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

Funding

National Natural Science Foundation of China-China Academy of General Technology Joint Fund for Basic Research http://dx.doi.org/10.13039/501100019492(51875331)