The application of fuzzy systems in the field of robotics has been a focal point for researchers and technologists. This article presents a scientometric study of fuzzy systems in robotics, primarily employing the CiteSpace tool. By analyzing a database comprising 729 collected articles, we conduct a detailed analysis of the major contribution areas, institutions, journals, high-frequency keywords, and keyword clustering related to the application of fuzzy systems in robotics. The results indicate significant progress in recent years across various aspects such as system modeling, perception, planning, decision-making, and control in the application of fuzzy systems in robotics. It is noteworthy that fuzzy control in robotics is transitioning from traditional controller design toward nonlinear and intelligent control, with a particular emphasis on addressing nonlinear problems using fuzzy systems, which is one of the current main research trends. This article provides a comprehensive mind map that can assist researchers in related fields and offers valuable insights for future research directions and developments.
BellmanREZadehLA. Decision-making in a fuzzy environment. Manage Sci1970; 17(4): B–141.
3.
ZadehLA. Fuzzy sets and information granularity. In: KlirGJYuanB (eds), Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers. Singapore: World Scientific, 1979, pp.433–448.
4.
ZadehLA. Fuzzy logic. Computer1988; 21(4): 83–93.
5.
ZadehLA. Fuzzy logic = computing with words. IEEE Trans Fuzzy Syst1996; 4(2): 103–111.
6.
BaraldiABlondaP. A survey of fuzzy clustering algorithms for pattern recognition. IEEE Trans Syst Man Cybern B1999; 29(6): 778–785.
7.
PedryczW. Fuzzy sets in pattern recognition: methodology and methods. Pattern Recognit1990; 23(1-2): 121–146.
8.
BezdekJC. Pattern recognition with fuzzy objective function algorithms. New York: Springer Science Business Media, 2013.
9.
MahmoodTUllahKKhanQ, et al. An approach toward decision-making and medical diagnosis problems using the concept of spherical fuzzy sets. Neural Comput Appl2019; 31(11): 7041–7053.
10.
PhuongNHKreinovichV. Fuzzy logic and its applications in medicine. Int J Med Inform2001; 62(2-3): 165–173.
11.
KrishnakumarK. Intelligent systems for aerospace engineering: an overview. In: Von Karman Institute lecture series on intelligent systems for aeronautics. Brussels: Ames Research Center2002.
12.
ZalnezhadESarhanAADHamdiM. A fuzzy logic based model to predict surface hardness of thin film tin coating on aerospace al7075-t6 alloy. Int J Adv Manuf Technol2013; 68(1-4): 415–423.
13.
ArdilC. Fuzzy uncertainty theory for stealth fighter aircraft selection in entropic fuzzy topsis decision analysis process. Int J Aerosp Mech Eng2022; 16(4): 88–97.
14.
FukudaTKubotaN. An intelligent robotic system based on a fuzzy approach. Proc IEEE1999; 87(9): 1448–1470.
15.
KahramanCDeveciMBoltürkE, et al. Fuzzy controlled humanoid robots: a literature review. Robot Auton Syst2020; 134: 103643.
16.
ZhengKHuYWuB. Intelligent fuzzy sliding mode control for complex robot system with disturbances. Eur J Control2020; 51: 95–109.
17.
DongCYuZChenX, et al. Adaptability control towards complex ground based on fuzzy logic for humanoid robots. IEEE Trans Fuzzy Syst2022; 30(6): 1574–1584.
18.
MendelJM. Fuzzy logic systems for engineering: a tutorial. Proc IEEE1995; 83(3): 345–377.
19.
WangTMTaoYLiuH. Current researches and future development trend of intelligent robot: a review. Int J Autom Comput2018; 15(5): 525–546.
20.
ZhangZCaoJ. Finite-time synchronization for fuzzy inertial neural networks by maximum value approach. IEEE Trans Fuzzy Syst2022; 30(5): 1436–1446.
21.
de Campos SouzaPV. Fuzzy neural networks and neuro-fuzzy networks: a review the main techniques and applications used in the literature. Appl Soft Comput2020; 92: 106275.
22.
AzadeganAPorobicLGhazinooryS, et al. Fuzzy logic in manufacturing: a review of literature and a specialized application. Int J Prod Econ2011; 132(2): 258–270.
23.
ChenC. Citespace II: detecting and visualizing emerging trends and transient patterns in scientific literature. J Am Soc Inf Sci Technol2006; 57(3): 359–377.
24.
ChenC. Science mapping: a systematic review of the literature. J Data Inf Sci2017; 2(2): 1–40.
25.
GandiaRMAntonialliFCavazzaBH, et al. Autonomous vehicles: scientometric and bibliometric review. Transp Rev2019; 39(1): 9–28.
26.
ChenCChenYHorowitzM, et al. Towards an explanatory and computational theory of scientific discovery. J Inform2009; 3(3): 191–209.
27.
SinghVKSinghPKarmakarM, et al. The journal coverage of web of science, scopus and dimensions: a comparative analysis. Scientometrics2021; 126(6): 5113–5142.
28.
GuoGLiX. A scientometric review of mobility-on-demand car-sharing systems. IEEE Intell Transp Syst Mag2023; 15(1): 212–229.
29.
MuthugalaMAVJSamarakoonSMBPMohan RayguruM, et al. Wall-following behavior for a disinfection robot using type 1 and type 2 fuzzy logic systems. Sensors2020; 20(16): 4445.
30.
MuthugalaMAVJVega-HerediaMMohanRE, et al. Design and control of a wall cleaning robot with adhesion-awareness. Symmetry2020; 12(1): 122.
31.
IbarraLWebbC. Advantages of fuzzy control while dealing with complex/unknown model dynamics: a quadcopter example. Appl Artif Intell2016; 31(1): 93–121.
32.
BoulkrouneAM’saadMFarzaM. Adaptive fuzzy system-based variable-structure controller for multivariable nonaffine nonlinear uncertain systems subject to actuator nonlinearities. Neural Comput Appl2017; 28(11): 3371–3384.
33.
GaoLWangDWangG. Further results on exponential stability for impulsive switched nonlinear time-delay systems with delayed impulse effects. Appl Math Comput2015; 268: 186–200.
34.
LiFGaoQ. Blow-up of solution for a nonlinear petrovsky type equation with memory. Appl Math Comput2016; 274: 383–392.
35.
JanaDKDasBMaitiM. Multi-item partial backlogging inventory models over random planninghorizon in random fuzzy environment. Appl Soft Comput2014; 21: 12–27.
36.
FengDSunMWangX. A family of conjugate gradient methods for large-scale nonlinear equations. J Inequal Appl2017; 2017(1): 236.
37.
ZhouQLiHShiP. Decentralized adaptive fuzzy tracking control for robot finger dynamics. IEEE Trans Fuzzy Syst2015; 23(3): 501–510.
38.
PengXShangYZhengX. Lower bounds for the blow-up time to a nonlinear viscoelastic wave equation with strong damping. Appl Math Lett2018; 76: 66–73. 2018.
39.
DuCYangCLiF, et al. Asynchronous output feedback control for fuzzy markovian jump systems via sliding mode. J Franklin Inst2019; 356(16): 8952–8970.
40.
DuCShiYLiF, et al. An improved co-design method of dynamical controller and asynchronous integral-type event-triggered mechanisms. IEEE Trans Syst Man Cybern Syst2023; 53(4): 2500–2509.
41.
ZhangHShiYSaadat MehrA. On h-inf filtering for discrete-time takagi–sugeno fuzzy systems. IEEE Trans Fuzzy Syst2012; 20(2): 396–401.
42.
ZhangHShiYWangJ. On energy-to-peak filtering for nonuniformly sampled nonlinear systems: a markovian jump system approach. IEEE Trans Fuzzy Syst2014; 22(1): 212–222.
43.
ZhangHWangJ. State estimation of discrete-time takagi–sugeno fuzzy systems in a network environment. IEEE Trans Cybern2015; 45(8): 1525–1536.
44.
LiuYChenRNaX, et al. Robust fault estimation of vehicular yaw rate sensor using a type-2 fuzzy approach. IEEE Trans Ind Electron2021; 68(10): 10029–10039.
45.
PanJNguyenATWangS, et al. Fuzzy unknown input observer for estimating sensor and actuator cyber-attacks in intelligent connected vehicles. Automot Innov2023; 6(2): 164–175.
46.
TaoSChenJZhouB, et al. Simultaneous robust state and sensor fault estimation of autonomous vehicle via synthesized design of dynamic and learning observers. IEEE Trans Vehicular Technol2024; 73(2): 1753–1764.
47.
ZhangHShiYLiuM. H-inf step tracking control for networked discrete-time nonlinear systems with integral and predictive actions. IEEE Trans Ind Inform2013; 9(1): 337–345.
48.
ZhangHShiYMuB. Optimal H-inf-based linear-quadratic regulator tracking control for discrete-time takagi–sugeno fuzzy systems with preview actions. J Dyn Syst Meas Control2013; 135(4): 044501.
49.
NguyenATSentouhCZhangH, et al. Fuzzy static output feedback control for path following of autonomous vehicles with transient performance improvements. IEEE Trans Intell Transp Syst2020; 21(7): 3069–3079.
50.
ZhangZZhangZZhangH. Distributed attitude control for multispacecraft via takagi–sugeno fuzzy approach. IEEE Trans Aerosp Electron Syst2018; 54(2): 642–654.
51.
ZhaoLGaoHKarimiHR. Robust stability and stabilization of uncertain t–s fuzzy systems with time-varying delay: an input–output approach. IEEE Trans Fuzzy Syst2013; 21(5): 883–897.
52.
ZhaoXZhangLShiP, et al. Novel stability criteria for T-S fuzzy systems. IEEE Trans Fuzzy Syst2014; 22(2): 313–323.
53.
WangYShenHKarimiHR, et al. Notice of violation of IEEE Publication Principles: dissipativity-based fuzzy integral sliding mode control of continuous-time T-S fuzzy systems. IEEE Trans Fuzzy Syst2018; 26(3): 1164–1176.
54.
WangYKarimiHRLamHK, et al. Fuzzy output tracking control and filtering for nonlinear discrete-time descriptor systems under unreliable communication links. IEEE Trans Cybern2020; 50(6): 2369–2379.
55.
WangYKarimiHRShenH, et al. Fuzzy-model-based sliding mode control of nonlinear descriptor systems. IEEE Trans Cybern2019; 49(9): 3409–3419.
56.
ShenHLiFYanH, et al. Finite-time event-triggered h-inf control for t–s fuzzy markov jump systems. IEEE Trans Fuzzy Syst2018; 26(5): 3122–3135.
57.
WeiYQiuJKarimiHR. Notice of violation of IEEE publication principles: reliable output feedback control of discrete-time fuzzy affine systems with actuator faults. IEEE Trans Circuits Syst I Regul Pap2017; 64(1): 170–181.
58.
WangYGaoYKarimiHR, et al. Notice of violation of IEEE publication principles: sliding mode control of fuzzy singularly perturbed systems with application to electric circuit. IEEE Trans Syst Man Cybern Syst2018; 48(10): 1667–1675.
59.
LiHWangJDuH, et al. Adaptive sliding mode control for takagi–sugeno fuzzy systems and its applications. IEEE Trans Fuzzy Syst2018; 26(2): 531–542.
60.
ZhaoYLiBQinJ, et al. H-inf consensus and synchronization of nonlinear systems based on a novel fuzzy model. IEEE Trans Cybern2013; 43(6): 2157–2169.
61.
WangLKarimiHRGuJ. Stability analysis for interval type-2 fuzzy systems by applying homogenous polynomially membership functions dependent matrices and switching technique. IEEE Trans Fuzzy Syst2021; 29(2): 203–212.
62.
LiHYinSPanY, et al. Model reduction for interval type-2 takagi–sugeno fuzzy systems. Automatica2015; 61: 308–314. 2015.
63.
NguyenATTaniguchiTEciolazaL, et al. Fuzzy control systems: past, present and future. IEEE Comput Intell Mag2019; 14(1): 56–68.
64.
BenderMJSimonovicSP. A fuzzy compromise approach to water resource systems planning under uncertainty. Fuzzy Sets Syst2000; 115(1): 35–44.
65.
DongPZhaoJLiuX, et al. Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: development stages, challenges, and future trends. Renew Sustain Energ Rev2022; 170: 112947.
66.
XuXZhaoJZhaoJ, et al. Comparative study on fuel saving potential of series-parallel hybrid transmission and series hybrid transmission. Energy Convers Manag2022; 252: 114970.
67.
WuWZhongXWuD, et al. Lio-fusion: reinforced lidar inertial odometry by effective fusion with gnss/relocalization and wheel odometry. IEEE Robot Autom Lett2023; 8(3): 1571–1578.
68.
WuDZhongXPengX, et al. Multimodal information fusion for high-robustness and low-drift state estimation of ugvs in diverse scenes. IEEE Trans Instrum Meas2022; 71: 1–15.
69.
GulerDYomraliogluT. Suitable location selection for the electric vehicle fast charging station with ahp and fuzzy ahp methods using gis. Ann GIS2020; 26(2): 169–189.
70.
ZhangSChenMZhangW, et al. Fuzzy optimization model for electric vehicle routing problem with time windows and recharging stations. Expert Syst Appl2020; 145: 113123.
71.
OkadaSSoperT. A shortest path problem on a network with fuzzy arc lengths. Fuzzy Sets Syst2000; 109(1): 129–140.
72.
ChuangTNKungJY. The fuzzy shortest path length and the corresponding shortest path in a network. Comput Oper Res2005; 32(6): 1409–1428.
73.
ZammoriFABragliaMFrosoliniM. A fuzzy multi-criteria approach for critical path definition. Int J Proj Manag2009; 27(3): 278–291.
74.
AmiriMGolozariF. Application of fuzzy multi-attribute decision making in determining the critical path by using time, cost, risk, and quality criteria. Int J Adv Manuf Technol2011; 54(1-4): 393–401.
75.
PereraLPCarvalhoJPGuedes SoaresC. Fuzzy logic based decision making system for collision avoidance of ocean navigation under critical collision conditions. J Mar Sci Technol2011; 16(1): 84–99.
76.
MehlawatMKGuptaP. A new fuzzy group multi-criteria decision making method with an application to the critical path selection. Int J Adv Manuf Technol2016; 83(5-8): 1281–1296.
77.
ZedamLJanNRakE, et al. An approach towards decision-making and shortest path problems based on t-spherical fuzzy information. Int J Fuzzy Syst2020; 22(5): 1521–1534.
78.
ZhaoTLiHDianS. Multi-robot path planning based on improved artificial potential field and fuzzy inference system1. J Intell Fuzzy Syst2020; 39(5): 7621–7637.
LiuBJiaoSShenY, et al. A dynamic hybrid trust network-based dual-path feedback consensus model for multi-attribute group decision-making in intuitionistic fuzzy environment. Inf Fusion2022; 80: 266–281.
81.
HentoutAMaoudjAAouacheM. A review of the literature on fuzzy-logic approaches for collision-free path planning of manipulator robots. Artif Intell Rev2023; 56(4): 3369–3444.
82.
SathiyaVChinnaduraiMRamabalanS. Mobile robot path planning using fuzzy enhanced improved multi-objective particle swarm optimization (fimopso). Expert Syst Appl2022; 198: 116875.
83.
LinZYueMChenG, et al. Path planning of mobile robot with pso-based apf and fuzzy-based dwa subject to moving obstacles. Trans Inst Meas Contr2022; 44(1): 121–132.
84.
PanJPackDKosakaA, et al. Fuzzy-nav: a vision-based robot navigation architecture using fuzzy inference for uncertainty-reasoning. In: Proc. IEEE world congress neural networks, vol. 2, pp. 602–607.
85.
KalaRShuklaATiwariR. Fusion of probabilistic a* algorithm and fuzzy inference system for robotic path planning. Artif Intell Rev2010; 33(4): 307–327.
86.
LeeMTarokhMCrossM. Fuzzy logic decision making for multi-robot security systems. Artif Intell Rev2010; 34(2): 177–194.
87.
Al-MutibKMattarEAlsulaimanM. Implementation of fuzzy decision based mobile robot navigation using stereo vision. Procedia Comput Sci2015; 62: 143–150.
88.
BakdiAHentoutABoutamiH, et al. Optimal path planning and execution for mobile robots using genetic algorithm and adaptive fuzzy-logic control. Robot Auton Syst2017; 89: 95–109.
89.
WuAYangRLiangX, et al. Visual range maneuver decision of unmanned combat aerial vehicle based on fuzzy reasoning. Int J Fuzzy Syst2022; 24(1): 519–536.
90.
DirikMCastilloOKocamazAF. Visual-servoing based global path planning using interval type-2 fuzzy logic control. Axioms2019; 8(2): 58.
91.
DirikMKocamazAFCastilloO. Global path planning and path-following for wheeled mobile robot using a novel control structure based on a vision sensor. Int J Fuzzy Syst2020; 22(6): 1880–1891.
92.
HessburgTTomizukaM. Fuzzy logic control for lateral vehicle guidance. IEEE Control Syst Mag1994; 14(4): 55–63.
93.
NaranjoJEGonzalezCReviejoJ, et al. Adaptive fuzzy control for inter-vehicle gap keeping. IEEE Trans Intell Transp Syst2003; 4(3): 132–142.
94.
NaranjoJSoteloMGonzalezC, et al. Using fuzzy logic in automated vehicle control. IEEE Intell Syst2007; 22(1): 36–45.
95.
FerreiraAAPomilioJASpiazziG, et al. Energy management fuzzy logic supervisory for electric vehicle power supplies system. IEEE Trans Power Electron2008; 23(1): 107–115.
96.
SharmaSObaidAJ. Mathematical modelling, analysis and design of fuzzy logic controller for the control of ventilation systems using matlab fuzzy logic toolbox. J Interdiscipl Math2020; 23(4): 843–849.
97.
JohanyákZTikkDKovácsS, et al. Fuzzy rule interpolation matlab toolbox-fri toolbox. In: 2006 IEEE international conference on fuzzy systems, pp. 351–357. IEEE.
98.
CastroJCastilloOMelinP. An interval type-2 fuzzy logic toolbox for control applications. In: 2007 IEEE international fuzzy systems conference, pp. 1–6. IEEE.
99.
OzekMBAkpolatZH. A software tool: type-2 fuzzy logic toolbox. Comput Appl Eng Educ2008; 16(2): 137–146.
100.
CastroJRCastilloOMelinP, et al. Building fuzzy inference systems with a new interval type-2 fuzzy logic toolbox. In: GavrilovaMLTanCJK (eds), Transactions on computational science I. Berlin: Springer-Verlag, 2008, pp. 104–114.
101.
CastilloOMelinPCastroJR. Computational intelligence software for interval type-2 fuzzy logic. Comput Appl Eng Educ2013; 21(4): 737–747.
102.
TaskinAKumbasarT. An open source matlab/simulink toolbox for interval type-2 fuzzy logic systems. In: 2015 IEEE symposium series on computational intelligence, pp. 1561–1568. IEEE.
103.
YingHSilerWBuckleyJJ. Fuzzy control theory: a nonlinear case. Automatica1990; 26(3): 513–520.
104.
HwangGCLinSC. A stability approach to fuzzy control design for nonlinear systems. Fuzzy Sets Syst1992; 48(3): 279–287.
105.
WangLX. Stable adaptive fuzzy control of nonlinear systems. IEEE Trans Fuzzy Syst1993; 1(2): 146–155.
106.
WangHOTanakaKGriffinMF. An approach to fuzzy control of nonlinear systems: stability and design issues. IEEE Trans Fuzzy Syst1996; 4(1): 14–23.
107.
LeeHJParkJBChenG. Robust fuzzy control of nonlinear systems with parametric uncertainties. IEEE Trans Fuzzy Syst2001; 9(2): 369–379.
108.
ZhangHLiuYWangY. Observer-based finite-time adaptive fuzzy control for nontriangular nonlinear systems with full-state constraints. IEEE Trans Cybern2021; 51(3): 1110–1120.
109.
ChangXHXiongJParkJH. Fuzzy robust dynamic output feedback control of nonlinear systems with linear fractional parametric uncertainties. Appl Math Comput2016; 291: 213–225.
110.
MirzajaniSAghababaMPHeydariA. Adaptive t–s fuzzy control design for fractional-order systems with parametric uncertainty and input constraint. Fuzzy Sets Syst2019; 365: 22–39.
111.
HuSYueDPengC, et al. Event-triggered controller design of nonlinear discrete-time networked control systems in ts fuzzy model. Appl Soft Comput2015; 30: 400–411.
112.
PanYYangGH. Event-triggered fuzzy control for nonlinear networked control systems. Fuzzy Sets Syst2017; 329: 91–107.
113.
SuXXiaFLiuJ, et al. Event-triggered fuzzy control of nonlinear systems with its application to inverted pendulum systems. Automatica2018; 94: 236–248.
114.
ChenZZhangBStojanovicV, et al. Event-based fuzzy control for ts fuzzy networked systems with various data missing. Neurocomputing2020; 417: 322–332.
115.
PanYWuYLamHK. Security-based fuzzy control for nonlinear networked control systems with dos attacks via a resilient event-triggered scheme. IEEE Trans Fuzzy Syst2022; 30(10): 4359–4368.
116.
ChenMWangHLiuX. Adaptive fuzzy practical fixed-time tracking control of nonlinear systems. IEEE Trans Fuzzy Syst2021; 29(3): 664–673.
117.
PanYDuPXueH, et al. Singularity-free fixed-time fuzzy control for robotic systems with user-defined performance. IEEE Trans Fuzzy Syst2021; 29(8): 2388–2398.
118.
LuKLiuZYuH, et al. Adaptive fuzzy inverse optimal fixed-time control of uncertain nonlinear systems. IEEE Trans Fuzzy Syst2022; 30(9): 3857–3868.
119.
SunYZhangL. Fixed-time adaptive fuzzy control for uncertain strict feedback switched systems. Inf Sci2021; 546: 742–752.
120.
JiaTPanYLiangH, et al. Event-based adaptive fixed-time fuzzy control for active vehicle suspension systems with time-varying displacement constraint. IEEE Trans Fuzzy Syst2022; 30(8): 2813–2821.
121.
WangHXuKQiuJ. Event-triggered adaptive fuzzy fixed-time tracking control for a class of nonstrict-feedback nonlinear systems. IEEE Trans Circuits Syst I Regul Pap2021; 68(7): 3058–3068.
122.
LuKLiuZWangY, et al. Fixed-time adaptive fuzzy control for uncertain nonlinear systems. IEEE Trans Fuzzy Syst2021; 29(12): 3769–3781.
123.
CuiDXiangZ. Nonsingular fixed-time fault-tolerant fuzzy control for switched uncertain nonlinear systems. IEEE Trans Fuzzy Syst2023; 31(1): 174–183.
124.
Sanchez-SolanoSCabreraAJBaturoneI, et al. FPGA implementation of embedded fuzzy controllers for robotic applications. IEEE Trans Ind Electron2007; 54(4): 1937–1945.
125.
MonmassonECirsteaMN. Fpga design methodology for industrial control systems—a review. IEEE Trans Ind Electron2007; 54(4): 1824–1842.
126.
SulaimanNObaidZAMarhabanMH, et al. Fpga-based fuzzy logic: design and applications—a review. Int J Eng Technol2009; 1(5): 491–503.
127.
PanduruK. Flcplus: a light-weight open-source library in c++ for fuzzy logic controller. Int J Comput Sci Commun2014; 5(1): 202–204.
128.
ZhangZHeHDengX. An FPGA-implemented antinoise fuzzy recurrent neural network for motion planning of redundant robot manipulators. IEEE Trans Neural Netw Learn Syst2023. Early Access. DOI: 10.1109/TNNLS.2023.3253801.
129.
García-MartínezJRCruz-MiguelEECarrillo-SerranoRV, et al. A PID-type fuzzy logic controller-based approach for motion control applications. Sensors2020; 20(18): 5323.
130.
NadaAABayoumiMA. Development of embedded fuzzy control using reconfigurable fpga technology. Autom2024; 65(2): 609–626.
