Electric Power Steering (EPS) systems are widely integrated into modern automobiles to enhance steering efficiency and safety. The performance of EPS systems heavily depends on the control strategies utilized, which are often developed for different specific purposes. However, no comprehensive scientific publication has thoroughly reviewed and analyzed the performance of the various control methods applied to EPS systems. In this article, we conduct a systematic review, analysis, and comparison of existing control approaches used in EPS systems, including recent developments in nonlinear robust, Artificial Intelligent (AI)-based and meta-heuristic control methods. Discussions and quantitative results are provided to reinforce the work’s contributions. Finally, the advantages and drawbacks of each control method are highlighted to identify their most suitable application scenarios. These efforts aim to provide valuable insights and serve as a reference for future research and practical implementation in EPS control, especially in the context of increasing interest in autonomous vehicles. It is anticipated that wider dissemination of recent developments in control strategies in EPS systems will stimulate further developments and will foster academic collaborations.
NguyenTA.Active disturbance rejection control based on soft computing techniques for electric power steering to improve system performance. PLoS One2025; 20(6): 1–23.
2.
NguyenTA.A novel approach to the FPIBSC strategy for an electric power steering system. Trans Inst Meas Control2024; 47(3): 555–571.
Dell’AmicoAKrusP.Modeling, simulation, and experimental investigation of an electrohydraulic closed-center power steering system. IEEE/ASME Trans Mechatr2015; 20(5): 2452–2462.
5.
GhimireRZhangCPattipatiKR.A rough set-theory-based fault-diagnosis method for an electric power-steering system. IEEE/ASME Trans Mechatr2018; 23(5): 2042–2053.
6.
WangZKarimiHR.Experimental study on antivibration control of electrical power steering systems. J Appl Math2014; 2014: 1–7.
7.
BaharomMBHussainKDayAJ.Design of full electric power steering with enhanced performance over that of hydraulic power-assisted steering. Proc Inst Mech Eng Part D: J Autom Eng2013; 227(3): 390–399.
8.
GuanXZhangY-NDuanC-G, et al. Study on decomposition and calculation method of EPS assist characteristic curve. Proc Inst Mech Eng Part D: J Autom Eng2021; 235(8): 2166–2175.
9.
RamasamyD.An approach for steering wheel angle evaluation without using a true power-on steering angle sensor in electric power steering systems. J Inst Eng (India) Series B2023; 104(3): 563–568.
10.
KhalkhaliAShojaeefardMHDahmardehM, et al. Optimal design and applicability of electric power steering system for automotive platform. J Central South Univ2019; 26(4): 839–851.
11.
FuZLuYZhaoD, et al. A novel dual power-driven electric power steering system for electric commercial vehicles. Adv Mech Eng2023; 15(9). https://doi.org/10.1177/16878132231200314
12.
JangBKimJHYangSM.Application of rack type motor driven power steering control system for heavy vehicles. Int J Autom Technol2016; 17(3): 409–414.
13.
FankemSWeiskircherTMüllerS.Model-based rack force estimation for electric power steering. IFAC Proc Vol2014; 47(3): 8469–8474.
14.
ChungSSLeeYZKimES.Development of the wear resistant ball nut assembly in the Rack-Based electric power steering. Int J Autom Technol2020; 21(3): 713–718.
15.
AnKBackJLeeS-K, et al. Active vibration control for reduction of interior noise caused by R-MDPS of electric power steering in electric vehicle. Appl Acoust2023; 211: 109544.
16.
NguyenTA.Mathematical modeling and characteristics of automotive electric power steering systems: a state–of–the–art literature review. Ain Shams Eng J2025; 16: 103516.
17.
ShiGSongMJuC, et al. Adaptive robust steering strategy for electro-hydraulic hybrid steering system based on backstepping method. Proc Inst Mech Eng Part D: J Autom Eng2023; 238(10–11): 3328–3346.
18.
ParkJIJeonKYiK.An investigation on the energy-saving effect of a hybrid electric-power steering system for commercial vehicles. Proc Inst Mech Eng Part D: J Autom Eng2018; 233(6): 1623–1648.
19.
LuanZZhaoWWangC.Mode switching and consistency control for electric-hydraulic hybrid steering system. Autom Innov2024; 7(1): 166–181.
20.
TanYKongHMaL, et al. Testing and optimization of noise of an electrohydraulic power steering pump. J Braz Soc Mech Sci Eng2021; 43(9). https://doi.org/10.1007/s40430-021-03120-3
21.
HassanMKAzubirNAMNizamHMI, et al. Optimal design of electric power assisted steering system (EPAS) using GA-PID method. Proc Eng2021; 41: 614–621.
22.
JungD.A minimally configured hardware-in-the-loop simulator of electrical power steering system for human driver interaction on crosswind effect. IEEE Access2021; 9: 60470–60481.
23.
TuranA.Improved PID control design for electric power steering DC motor. IEEE Access2025; 13: 6080–6088.
24.
HanifahRATohaSFHassanMK, et al. Power reduction optimization with swarm based technique in electric power assist steering system. Energy2016; 102: 444–452.
25.
HanifahRATohaSFAhmadS, et al. Swarm-intelligence tuned current reduction for Power-Assisted steering control in electric vehicles. IEEE Trans Indus Elect2017; 65(9): 7202–7210.
26.
ZhengZWeiJ.Research on electric power steering fuzzy PI control strategy based on phase compensation. Int J Dyn Control2022; 11(4): 1867–1879.
27.
DaiCChenGZongC, et al. Precise compound control of loading force for electric load simulator of electric power steering test bench. Chin J Mech Eng2022; 35(1): 8.
28.
CaoZZhengS.MR-SAS and electric power steering variable universe fuzzy PID integrated control. Neural Comput Appl2017; 31(4): 1249–1258.
29.
LiYWuGWuL, et al. Electric power steering nonlinear problem based on proportional–integral–derivative parameter self-tuning of back propagation neural network. Proc Inst Mech Eng Part C: J Mech Eng Sci2020; 234(23): 4725–4736.
30.
Ramos-FernándezJCLópez-MoralesVMárquez-VeraMA, et al. Neuro-fuzzy modelling and stable PD controller for angular position in steering systems. Int J Autom Technol2021; 22(6): 1495–1503.
31.
LiYFengQZhangY, et al. Dual-motor full-weight electric power steering system for commercial vehicle. Int J Autom Technol2019; 20(3): 477–486.
32.
NguyenTATranTTHHoangTB.Design a new algorithm (iL-GA) to optimize controller parameters for an automotive suspension system. World J Eng Jun. 2024; 22(4): 711–724.
33.
FengXHuJ.Discrete fuzzy adaptive PID control algorithm for automotive anti-lock braking system. J Ambient Intell Human Comput2021. https://doi.org/10.1007/s12652-020-02829-8
34.
SaleemOAliSIqbalJ.Robust MPPT control of stand-alone photovoltaic systems via adaptive self-adjusting fractional order PID controller. Energies2023; 16(13): 5039.
35.
NguyenTA.Applying a PID-SMC synthetic control algorithm to the active suspension system to ensure road holding and ride comfort. PLoS One2023; 18(10): e0283905.
36.
SaleemOAhmadKRIqbalJ.Fuzzy-augmented model reference adaptive PID control law design for robust voltage regulation in DC–DC buck converters. Mathematics2024; 12(12): 1893.
37.
MancaRCircostaSKhanI, et al. Performance assessment of an electric power steering system for driverless formula student vehicles. Actuators2021; 10(7): 165.
38.
IqbalJAhmadOMalikA. HEXOSYS II - towards realization of light mass robotics for the hand. In: 2011 IEEE 14th international multitopic conference, Karachi, Pakistan, 2011, pp. 115–119.
39.
GuanXZhangYLuP, et al. The control strategy of the electric power steering system for steering feel control. Proc Inst Mech Eng Part D: J Autom Eng2022; 238(2–3): 347–357.
40.
GovenderVOrtmannLMüllerS.Synthesis and validation of a rack position controller for an electric power steering. IFAC-PapersOnLine2017; 50(2): 253–258.
41.
LeeDLoYGHanM, et al. A development of the model based torque feedback control with disturbance observer for electric power steering system. SAE Tech Papers2019. https://doi.org/10.4271/2019-01-1233
42.
DiabAValmorbidaGPasillas-LépineW.Linear assistance filter design for electric power steering systems with improved delay margin. Int J Control2023; 97(5): 1118–1135.
43.
NguyenDNNguyenTA.Proposing an original control algorithm for the active suspension system to improve vehicle vibration: adaptive fuzzy sliding mode proportional-integral-derivative tuned by the fuzzy (AFSPIDF). Heliyon2023; 9(3): e14210.
44.
SaleemOHamzaAIqbalJ.A fuzzy-immune-regulated single-neuron proportional–integral–derivative control system for robust trajectory tracking in a lawn-mowing robot. Computers2024; 13(11): 301.
45.
AbeTFujimuraYHiroseT, et al. Linear quadratic control design in electric power steering system. In: International conference on advanced mechatronic systems (ICAMechS), 30 November–3 December 2016, pp. 73–78.
46.
ChituCLacknerJHornM, et al. Controller design for an electric power steering system based on LQR techniques. COMPEL Int J Comput Math Elect Electr Eng2013; 32(3): 763–775.
47.
IrmerMHenrichfreiseH.Design of a robust LQG compensator for an electric power steering. IFAC-PapersOnLine2020; 53(2): 6624–6630.
48.
MehrabiNMcPheeJAzadNL.Design and evaluation of an observer-based disturbance rejection controller for electric power steering systems. Proc Inst Mech Eng Part D: J Autom Eng2015; 230(7): 867–884.
49.
LiuXPangHShangY, et al. Optimal design of fault-tolerant controller for an electric power steering system with sensor failures using genetic algorithm. Shock Vibr2018; 2018(1). https://doi.org/10.1155/2018/1801589
50.
NguyenTA.A linear quadratic tracking control strategy based on fuzzy for electric power steering systems. Adv Mech Eng2025; 17(2). https://doi.org/10.1177/16878132251323407
51.
NguyenMLTranTTHNguyenTA, et al. Application of MIMO control algorithm for active suspension system: a new model with 5 state variables. Latin Am J Solids Struct2022; 19(2). https://doi.org/10.1590/1679-78256992
52.
SaxenaADashAKVirmaniR, et al. Design of LQR-based FLC for the optimal regenerative braking controlling of solar PV-based electric vehicle system. IETE J Res2023; 70(2): 1923–1936.
53.
AmerNH, et al. Integrated torque-vectoring and anti-roll moment distribution strategies based on optimal control: influence of model complexity and road curvature preview. Veh Syst Dyn2024; 61(10): 2533–2566.
54.
SaleemOAbbasFIqbalJ.Complex fractional-order LQIR for inverted-pendulum-type robotic mechanisms: design and experimental validation. Mathematics2023; 11(4): 913.
55.
SaleemOIqbalJAlharbiS.Self-regulating fuzzy-LQR control of an inverted-pendulum system using reconfigurable hyperbolic functions for intermediate error modulation. Machines2025; 13(10): 939.
56.
MuriloARodriguesRTeixeiraELS, et al. Design of a parameterized model predictive control for electric power assisted steering. Control Eng Pract2019; 90: 331–341.
57.
FujimuraYHashimotoSBanjerdpongchaiD. Design of model predictive control with nonlinear disturbance observer for electric power steering system. In: 2019 SICE International Symposium on Control Systems (SICE ISCS), 10–13 September 2019, pp. 49–56.
58.
LeeJChangH-J.Multi-parametric model predictive control for autonomous steering using an electric power steering system. Proc Inst Mech Eng Part D: J Autom Eng2019; 233(13): 3391–3402.
59.
JieCYanlingG.Research on control strategy of the electric power steering system for all-terrain vehicles based on model predictive current control. Math Prob Eng2021; 2021: 1–15.
60.
KhanMFIslamRUIqbalJ.Control strategies for robotic manipulators. In IEEE international conference on robotics and artificial intelligence, Rawalpindi, Pakistan, 22–23 October 2012, pp. 26–33.
61.
ZhaoWZhangH.Coupling control strategy of force and displacement for electric differential power steering system of electric vehicle with motorized wheels. IEEE Trans Veh Technol2018; 67(9): 8118–8128.
62.
ZhaoW-ZLiY-JWangC-Y, et al. H∞ control of novel active steering integrated with electric power steering function. J Central South Univ2013; 20(8): 2151–2157.
63.
PangHXuGSongZ, et al. EPS system of tractor automatic driving: an improved LMI with mixed sensitivity control method. J Elect Comput Eng2021; 2021: 1–13.
64.
DannöhlCMüllerSUlbrichH.H∞-control of a rack-assisted electric power steering system. Veh Syst Dyn2011; 50(4): 527–544.
65.
YangNT.A new control framework of electric power steering system based on admittance control. IEEE Trans Control Syst Technol2014; 23(2): 762–769.
66.
AllousMMrabetKZanzouriN.Fast fault-tolerant control of electric power steering systems in the presence of actuator fault. Proc Inst Mech Eng Part D: J Autom Eng2018; 233(12): 3088–3097.
67.
XueMXuGFengJ, et al. Control strategy of automotive electric power steering system based on generalized internal model control. J Algor Comput Technol2020; 14: 174830262093131.
68.
BaekJKangC.Time-delayed control for automated steering wheel tracking of electric power steering systems. IEEE Access2020; 8: 95457–95464.
69.
SorialRRSolimanMHHasanienHM, et al. A vector controlled drive system for electrically power assisted steering using Hall-Effect sensors. IEEE Access2021; 9: 116485–116499.
70.
XuHZhaoYLinF, et al. Integrated optimization design of electric power steering and suspension systems based on hierarchical coordination optimization method. Struct Multidisc Optim2022; 65(2): 59.
71.
LeeDKimK-SKimS.Controller design of an electric power steering system. IEEE Trans Control Syst Technol2017; 26(2): 748–755.
72.
NguyenDNNguyenTA.Proposing a BSPID control strategy considering external disturbances for electric Power steering (EPS) systems. IEEE Access2023; 11: 143230–143249.
73.
NguyenTAIqbalJ.Genetic algorithm inspired optimal integrated nonlinear control technique for an electric power steering system. J Braz Soc Mech Sci Eng2024; 46(11): 661.
74.
NguyenTA.Development of a novel integrated control strategy for automotive electric power steering Systems. IEEE Trans Autom Sci Eng2024; 22: 926–943.
75.
NguyenDNNguyenTA.Fuzzy backstepping control to enhance electric power steering system performance. IEEE Access2024; 12: 88681–88695.
76.
NguyenTA.Proposing a novel nonlinear integrated control technique for an electric power steering system to improve automotive dynamic stability. Proc Inst Mech Eng Part K: J Multibody Dyn2024; 238(3): 445–460.
77.
KimWSonYSChungCC.Torque-overlay-based robust steering wheel angle control of electrical power steering for a Lane-Keeping system of automated vehicles. IEEE Trans Veh Technol2015; 65(6): 4379–4392.
78.
AwanZSAliKIqbalJ, et al. Adaptive backstepping based sensor and actuator fault tolerant control of manipulator. J Elect Eng Technol2019; 14: 2497–2504.
79.
MaroufADjemaiMSentouhC, et al. A new control strategy of an electric-power-assisted steering system. IEEE Trans Veh Technol2012; 61(8): 3574–3589.
80.
NguyenTA.A new approach to an optimal integrated control strategy for electric steering systems. Ain Shams Eng J2024; 15(8): 102881.
81.
NguyenTAIqbalJ.Improving stability and adaptability of automotive electric steering systems based on a novel optimal integrated algorithm. Eng Comput2024; 41(4): 991–1034.
82.
KimSChoiMSungJ.Experimental verifications of electric power steering controller based on discrete-time sliding mode control with disturbance observer. Int J Autom Technol2023; 24(3): 883–888.
83.
KimGYouSLeeS, et al. Robust nonlinear torque control using steering wheel torque model for electric power steering system. IEEE Trans Veh Technol2023; 72(7): 9555–9560.
84.
LiYShimTWangD, et al. Model reference control-based steering feel improvement for electric power-assisted steering system. J Dyn Syst Meas Control2022; 145(4): 044501.
85.
NguyenTA.Designing a new integrated control solution for electric power steering systems based on a combination of nonlinear techniques. PLoS One2024; 19(9): e0308530.
86.
LuSLianMLiuM, et al. Adaptive fuzzy sliding mode control for electric power steering system. J Mech Sci Technol2017; 31(6): 2643–2650.
87.
JeongYWChungCCKimW.Nonlinear hybrid impedance control for steering control of Rack-Mounted electric power steering in autonomous vehicles. IEEE Trans Intell Transp Syst2019; 21(7): 2956–2965.
88.
LinF-JChenS-GSunI-F.Intelligent sliding-mode position control using recurrent wavelet fuzzy neural network for electrical power steering system. Int J Fuzzy Syst2017; 19(5): 1344–1361.
89.
LinF-JHungY-CRuanK-C.An intelligent second-order sliding-mode control for an electric power steering system using a wavelet fuzzy neural network. IEEE Trans Fuzzy Syst2014; 22(6): 1598–1611.
90.
LeeDYiKChangS, et al. Robust steering-assist torque control of electric-power-assisted-steering systems for target steering wheel torque tracking. Mechatronics2017; 49: 157–167.
91.
NguyenTA.Fuzzy sliding mode control based on an adaptive sliding surface and extended state observer for automotive electric power steering. Adv Mech Eng2025; 17(1). https://doi.org/10.1177/16878132251315517
92.
KhasawnehLDasM.A robust electric power-steering-angle controller for autonomous vehicles with disturbance rejection. Electronics2022; 11(9): 1337.
93.
AliKUllahSMehmoodA, et al. Adaptive FIT-SMC approach for an anthropomorphic manipulator with robust exact differentiator and neural network-based friction compensation. IEEE Access2022; 10: 3378–3389.
94.
NguyenTAIqbalJTranTTH, et al. Application of hybrid control algorithm on the vehicle active suspension system to reduce vibrations. Adv Mech Eng2024; 16(3). https://doi.org/10.1177/16878132241239816
95.
UllahMIAjwadSAIrfanM, et al. Non-linear control law for articulated serial manipulators: simulation augmented with hardware implementation. Elektron Elektrotech2016; 22(1): 3–7.
96.
NguyenTA.A new approach to selecting optimal parameters for the sliding mode algorithm on an automotive suspension system. Complexity2023; 2023: 1–14.
97.
JavaidUMehmoodAArshadA, et al. Operational efficiency improvement of PEM fuel cell—a sliding mode based modern control approach. IEEE Access2020; 8: 95823–95831.
98.
IqbalJUllahMIKhanAA, et al. Towards sophisticated control of robotic manipulators: experimental study on a pseudo-industrial arm. Stroj Vestn J Mech Eng2015; 61(7–8): 465–470.
99.
UllahNMehmoodYAslamJ, et al. UAVs-UGV leader follower formation using adaptive non-singular terminal super twisting sliding mode control. IEEE Access2021; 9: 74385–74405.
100.
ZhengZWeiJ.Research on active disturbance rejection control strategy of electric power steering system under extreme working conditions. Meas Control2023; 57(1): 90–100.
101.
NaSLiZQiuF, et al. Torque control of electric power steering systems based on improved active disturbance rejection control. Math Prob Eng2020; 2020: 1–13.
102.
MaXGuoYChenL.Active disturbance rejection control for electric power steering system with assist motor variable mode. J Frank Inst2018; 355(3): 1139–1155.
103.
NguyenTANguyenTL.Nonlinear active disturbance rejection mechanism based sliding mode control for enhancing electric power assisted steering performance. PLoS One2025; 20(4): e0321664.
104.
NguyenTA.Active disturbance rejection control for reducing vehicle body displacement based on an extended state observer and dynamic fuzzy techniques. PLoS One2025; 20(1): e0313104.
105.
ZhaoLWangNXiaG, et al. Regenerative braking torque compensation control based on improved ADRC. Proc Inst Mech Eng Part D: J Autom Eng2023; 238(6): 1315–1329.
106.
LuoSZhangBMaJ, et al. Research on a hierarchical control strategy for anti-lock braking systems based on Active Disturbance Rejection Control (ADRC). Appl Sci2025; 15(3): 1294.
107.
FengXLiuSYuanQ, et al. Research on wheel-legged robot based on LQR and ADRC. Sci Rep2023; 13(1): 15122.
108.
ZhouJLiYZhangJ, et al. ADRC control of ultra-high-Speed electric air compressor considering excitation observation. Actuators2024; 13(10): 420.
109.
IrfanSZhaoLUllahS, et al. Differentiator- and observer-based feedback linearized advanced nonlinear control strategies for an unmanned aerial vehicle system. Drones2024; 8(10): 527.
110.
ParmarMHungJY.A sensorless optimal control system for an automotive electric power assist steering system. IEEE Trans Indus Electr2004; 51(2): 290–298.
111.
JungDKimS.A novel control strategy of crosswind disturbance compensation for Rack-Type Motor Driven Power Steering (R-MDPS) system. IEEE Access2022; 10: 125148–125166.
112.
LeeDJangBHanM, et al. A new controller design method for an electric power steering system based on a target steering torque feedback controller. Control Eng Pract2020; 106: 104658.
113.
LiSGuoLCuiG, et al. Return control of electronic power steering unequipped with an angle sensor. Int J Autom Technol2019; 20(2): 349–357.
114.
AhmadSUppalAAAzamMR, et al. Chattering free sliding mode control and state dependent Kalman filter design for underground gasification energy conversion process. Electronics2023; 12(4): 876.
115.
KimSJungD-Y.Fault estimation of rack-driving motor in electrical power steering system using an artificial neural network observer. Electronics2022; 11(24): 4149.
116.
SuXXiaoB.Actuator-integrated fault estimation and fault tolerant control for electric power steering system of forklift. Appl Sci2021; 11(16): 7236.
117.
SaifiaDChadliMKarimiHR, et al. Fuzzy control for electric power steering system with assist motor current input constraints. J Frank Inst2014; 352(2): 562–576.
118.
YaohuaLJikangFJieH, et al. Novel electric power steering control strategies of commercial vehicles considering adhesion coefficient. Adv Mech Eng2020; 12(12): 168781402098305.
119.
FuDRakhejaSShangguanW-B, et al. Robust control for fuzzy electric power steering system: a two-layer performance approach. J Vibr Control2021; 28(5–6): 536–550.
120.
LiXZhaoX-PChenJ.Controller design for electric power steering system using T-S fuzzy model approach. Int J Autom Comput2009; 6(2): 198–203.
121.
NasriMSaifiaDChadliM, et al. H∞ static output feedback control for electrical power steering subject to actuator saturation via fuzzy Lyapunov functions. Trans Inst Meas Control2019; 41(12): 3340–3351.
122.
YouSKimGLeeS, et al. Neural approximation-based adaptive control using reinforced gain for steering wheel torque tracking of electric power steering system. IEEE Trans Syst Man Cybernet Syst2023; 53(7): 4216–4225.
123.
HartonoRChaHRShinKJ.Design of electric power steering system identification and control for autonomous vehicles based on artificial neural network. IEEE Access2024; 12: 108460–108471.
124.
LiYYangZZhaiD, et al. Study on control strategy of electric power steering for commercial vehicle based on multi-map. World Elect Veh J2023; 14(2): 33.
125.
AmirkhaniAShirzadehMHeydariJ.Automotive electric power steering control with robust observer based neuroadaptive Type-2 radial basis function methodology. IEEE Open J Veh Technol2024; 5: 592–605.
126.
HungY-CLinF-JHwangJ-C, et al. Wavelet fuzzy neural network with asymmetric membership function controller for electric power steering system via improved differential evolution. IEEE Trans Power Elect2014; 30(4): 2350–2362.
127.
NguyenTA.Establishing a novel adaptive fuzzy control algorithm for an active stabilizer bar with complex automotive dynamics model. Ain Shams Eng J2023; 15(1): 102334.
128.
NguyenTA.Adaptive neuro-fuzzy inference control for active stabilizer bars based on multiple data sources. Sci Prog2024; 107(3). https://doi.org/10.1177/00368504241274976
129.
NguyenTA.Proposing a novel fuzzy control algorithm for automotive active stabilizer bars based on views of roll stability and ride comfort. Proc Inst Mech Eng Part K: J Multibody Dyn2023; 237(4): 585–600.
130.
SwethamaraiPLakshmiP.Adaptive-fuzzy fractional order PID controller-based active suspension for vibration control. IETE J Res2020; 68(5): 3487–3502.
131.
WangHLuYTianY, et al. Fuzzy sliding mode based active disturbance rejection control for active suspension system. Proc Inst Mech Eng Part D: J Autom Eng2019; 234(2–3): 449–457.
132.
NguyenTA.A novel approach with a fuzzy sliding mode proportional integral control algorithm tuned by fuzzy method (FSMPIF). Sci Rep2023; 13(1): 7327.
133.
FernandezJPVargasMAGarciaJMV, et al. Coevolutionary optimization of a fuzzy logic controller for antilock braking systems under changing road conditions. IEEE Trans Veh Technol2021; 70(2): 1255–1268.
134.
MaiaRMendesJAraújoR, et al. Regenerative braking system modeling by fuzzy Q-Learning. Eng Appl Artif Intell2020; 93: 103712.
135.
NguyenTADoTMNTranTTH, et al. Self-learning adaptive neuro-fuzzy approximation of robust control behavior in electric power steering systems. PLoS One2025; 20(10): e0334539.
136.
AlabeLWKeaKHanY, et al. A deep learning approach to detect anomalies in an electric power steering system. Sensors2022; 22(22): 8981.
