In this article, we propose an efficient intelligent decision method for a bionic motion unmanned system to simulate the formation change during the hunting process of the wolves. Path planning is a burning research focus for the unmanned system to realize the formation change, and some traditional techniques are designed to solve it. The intelligent decision based on evolutionary algorithms is one of the famous path planning approaches. However, time consumption remains to be a problem in the intelligent decisions of the unmanned system. To solve the time-consuming problem, we simplify the multi-objective optimization as the single-objective optimization, which was regarded as a multiple traveling salesman problem in the traditional methods. Besides, we present the improved genetic algorithm instead of evolutionary algorithms to solve the intelligent decision problem. As the unmanned system’s intelligent decision is solved, the bionic motion control, especially collision avoidance when the system moves, should be guaranteed. Accordingly, we project a novel unmanned system bionic motion control of complex nonlinear dynamics. The control method can effectively avoid collision in the process of system motion. Simulation results show that the proposed simplification, improved genetic algorithm, and bionic motion control method are stable and effective.
RenC-EChenLChenCLP. Adaptive fuzzy leader-following consensus control for stochastic multiagent systems with heterogeneous nonlinear dynamics. IEEE T Fuzzy Syst2017; 25(1): 181–190.
2.
YuDChenCLPRenC-E, et al. Swarm control for self-organized system with fixed and switching topology. IEEE T Cybernetics2020; 50: 4481–4494.
3.
YuDChenCLP. Smooth transition in communication for swarm control with formation change. IEEE T Ind Inform2020; 16: 6962–6971.
4.
YuDChenCLP. Distributed generation and control of persistent formation for multi-agent systems. In: Proceedings of the 2017 4th international conference on information, cybernetics and computational social systems (ICCSS), Dalian, China, 24–26 July 2017, pp.142–147. New York: IEEE.
5.
PengZWangDHuXJ.Robust adaptive formation control of underactuated autonomous surface vehicles with uncertain dynamics. IET Control Theory A2011; 5(12): 1378–1387.
6.
LiT-SFengGWangD, et al. Neural-network-based simple adaptive control of uncertain multi-input multi-output non-linear systems. IET Control Theory A2010; 4(9): 1543–1557.
7.
ZhangYNingXWangZ, et al. Super-twisting disturbance observer–based fuzzy adaptive finite-time control for a class of space unmanned systems with time-varying output constraints. Proc IMechE, Part I: J Systems and Control Engineering2021; 235: 1583–1593.
8.
DongYZhaoSZhangH, et al. A self-management mechanism for noncooperative behaviors in large-scale group consensus reaching processes. IEEE T Fuzzy Syst2018; 26(6): 3276–3288.
9.
CapuanoNChiclanaFFujitaH, et al. Fuzzy group decision making with incomplete information guided by social influence. IEEE T Fuzzy Syst2018; 26(3): 1704–1718.
10.
PérezIJCabrerizoFJAlonsoS, et al. A new consensus model for group decision making problems with non-homogeneous experts. IEEE T Syst Man Cy: S2014; 44(4): 494–498.
11.
MataFMartínezLHerrera-ViedmaE.An adaptive consensus support model for group decision-making problems in a multigranular fuzzy linguistic context. IEEE T Fuzzy Syst2009; 17(2): 279–290.
12.
KaratasIHaliciogluRFedaiY.Investigation of hybrid electric vehicle control systems. Int Adv Res Eng J2017; 1(1): 11–17.
13.
AldemirAAnwerMS.Determination of optimal PID control parameters by response surface methodology. Int Adv Res Eng J2020; 4(2): 142–153.
14.
HanHZhangLWuX, et al. An efficient second-order algorithm for self-organizing fuzzy neural networks. IEEE T Cybernetics2019; 49(1): 14–26.
15.
HanHLuWZhangL, et al. Adaptive gradient multiobjective particle swarm optimization. IEEE T Cybernetics2018; 48(11): 3067–3079.
16.
XuYFangMPanY-J, et al. Event-triggered output synchronization for nonhomogeneous agent systems with periodic denial-of-service attacks. Int J Robust Nonlin2021; 31: 1851–1865.
17.
VeresSMMolnarLLincolnNK, et al. Autonomous vehicle control systems — a review of decision making. Proc IMechE, Part I: J Systems and Control Engineering2011; 225(2): 155–195.
18.
GümüşçüATenekeciMETabanlıoğluA.The shortest path detection for unmanned aerial vehicles via genetic algorithm on aerial imaging of agricultural lands. Int Adv Res Eng J2018; 2(3): 315–319.
19.
YuDChenCLPXuH.Intelligent decision making and bionic movement control of self-organized swarm. IEEE T Ind Electron2021; 68: 6369–6378.
20.
RenC-EChenCLP. Sliding mode leader-following consensus controllers for second-order non-linear multi-agent systems. IET Control Theory A2015; 9(10): 1544–1552.
21.
LiuXSuXSongY-D, et al. Switched control of repeated scalar non-linear systems via sliding mode control technique. IET Control Theory A2017; 11(8): 1088–1097.
22.
SuiSTongSLiY.Adaptive fuzzy decentralised output feedback control of pure-feedback large-scale stochastic non-linear systems with unknown dead zone. IET Control Theory A2014; 8(7): 488–502.
23.
HuangLLiYTongS.Command filter-based adaptive fuzzy backstepping control for a class of switched non-linear systems with input quantisation. IET Control Theory A2017; 11(12): 1948–1958.
24.
HanH-GWuX-LLiuZ, et al. Design of self-organizing intelligent controller using fuzzy neural network. IEEE T Fuzzy Syst2018; 26(5): 3097–3111.
25.
YuDChenCLPXuH.Fuzzy swarm control based on sliding-mode strategy with self-organized omnidirectional mobile robots system. IEEE T Syst Man Cy: S. Epub ahead of print 20January2021. DOI: 10.1109/TSMC.2020.3048733.
26.
YuDXuHChenCLP, et al. Dynamic coverage control based on k-means. IEEE T Ind Electron. Epub ahead of print 19May2021. DOI: 10.1109/TIE.2021.3080205.
27.
LiuJHanWWangX, et al. Research on cooperative trajectory planning and tracking problem for multiple carrier aircraft on the deck. IEEE Syst J2020; 14(2): 3027–3038.
28.
PengHJiangX.Nonlinear receding horizon guidance for spacecraft formation reconfiguration on libration point orbits using a symplectic numerical method. ISA T2016; 60: 38–52.
29.
PengHWangW.Adaptive surrogate model-based fast path planning for spacecraft formation reconfiguration on libration point orbits. Aerosp Sci Technol2016; 54: 151–163.
30.
PengHZhaoJWuZ, et al. Optimal periodic controller for formation flying on libration point orbits. Acta Astronaut2011; 69(7–8): 537–550.
31.
WenGChenCLPLiuY-J.Formation control with obstacle avoidance for a class of stochastic multiagent systems. IEEE T Ind Electron2018; 65(7): 5847–5855.
32.
LiMPengHZhongW.Optimal control of loose spacecraft formations near libration points with collision avoidance. Nonlinear Dynam2016; 83(4): 2241–2261.
33.
MondalABeheraLSahooSR, et al. A novel multi-agent formation control law with collision avoidance. IEEE/CAA J Automat Sin2017; 4(3): 558–568.
34.
MondalABhowmickCBeheraL, et al. Trajectory tracking by multiple agents in formation with collision avoidance and connectivity assurance. IEEE Syst J2018; 12(3): 2449–2460.
35.
XiaYNaXSunZ, et al. Formation control and collision avoidance for multi-agent systems based on position estimation. ISA T2016; 61: 287–296.
36.
HanHWuXQiaoJ.A self-organizing sliding-mode controller for wastewater treatment processes. IEEE T Contr Syst T2019; 27(4): 1480–1491.
37.
DongSLiuMWuZ-G, et al. Observer-based sliding mode control for Markov jump systems with actuator failures and asynchronous modes. IEEE T Circuits: II2021; 68: 1967–1971.
38.
WangQWuZ-G.Robust output feedback control for input-saturated systems based on a sliding mode observer. Circ Syst Signal Pr2021; 40: 2267–2281.
