Advanced Driver Assistance Systems (ADASs) hold the potential to significantly reduce the number of road fatalities while enhancing vehicle safety and stability. A prominent feature of ADAS is the Lane Keep Assistance System (LKAS), designed to prevent unintended lane departures and ensure vehicles stay within their designated lanes, thereby safeguarding passengers. This research is centered on developing a lead/lag compensator for the robust design of a PID controller dedicated to lane keeping assistance control. The compensator-PD controller enhances lane tracking performance while ensuring vehicle stability is maintained. The gains of the PD controller are tuned using the Ziegler–Nichols method to achieve robustness in presence of road disturbances. The performance of the proposed controller was compared with that of a PID controller without any compensator and a fuzzy PID controller across variable speed conditions and the presence of sharp road curvature disturbances. Based on extensive simulation studies in MATLAB/SIMULINK environment the performance of all above controllers was evaluated. The proposed compensator based controller outperforms the existing designs by ensuring minimum tracking error.
AllouSZennirYBelmeguenaiA (2017) Fuzzy logic controller for autonomous vehicle path tracking. In: 2017 18th international conference on sciences and techniques of automatic control and computer engineering (STA), Monastir, Tunisia, 21–23 December 2017, pp. 328–333. IEEE.
2.
AngKHChongGLiY (2005) Pid control system analysis, design, and technology. IEEE Transactions on Control Systems Technology13: 559–576.
3.
AsadiFAbutN (2021) Kharitonov’s theorem: a good starting point for robust control. The International Journal of Electrical Engineering & Education58(1): 57–82.
4.
ChenYBhaskaranTXueD (2008) Practical tuning rule development for fractional order proportional and integral controllers. Journal of Computational and Nonlinear Dynamics3(2): 78–82.
5.
DiabMKAmmarHHShalabyRE (2020) Self-driving car lane-keeping assist using pid and pure pursuit control. In: 2020 international conference on innovation and intelligence for informatics, computing and technologies (3ICT), Sakheer, Bahrain, 20–21 December 2020, pp. 1–6. IEEE.
6.
DongXPeiHGanM (2021) Autonomous vehicle lateral control based on fractional-order pid. In:2021 IEEE 5th Information technology, networking, electronic and automation control conference (ITNEC), Xi’an, China, 15–17 October 2021. Vol. 5, pp. 830–835.
7.
EfheijHAlbagulA (2021) Comparison of pid and artificial neural network controller in on line of real time industrial temperature process control system. In: 2021 IEEE 1st international maghreb meeting of the conference on sciences and techniques of automatic control and computer engineering MI-STA, Tripoli, Libya, 25-27 May 2021, pp. 110–115. IEEE.
8.
FarivarFHaghighiMSJolfaeiA, et al. (2021) Covert attacks through adversarial learning: study of lane keeping attacks on the safety of autonomous vehicles. IEEE/ASME Transactions on Mechatronics26: 1350–1357.
9.
HanGFuWWangW, et al. (2017) The lateral tracking control for the intelligent vehicle based on adaptive pid neural network. Sensors17: 1244.
10.
HuCJingHWangR, et al. (2016) Robust h output-feedback control for path following of autonomous ground vehicles. Mechanical Systems and Signal Processing70–71: 414–427.
11.
KebbatiYPuigVAit-OufroukhN, et al. (2021) Optimized adaptive mpc for lateral control of autonomous vehicles. In: 2021 9th International Conference on Control, Mechatronics and Automation (ICCMA), Belval, Luxembourg, 11–14 November 2021, pp. 95–103. IEEE.
12.
KharitonovV (1978) Asymptotic stability of an equilibrium position of a family of systems of linear differential equations. Differential’nye Uraveniya14: 1483–1485.
13.
LalDSVivekASelvarajG (2017) Lateral control of an autonomous vehicle based on pure pursuit algorithm. In: 2017 international conference on technological advancements in power and energy (TAP energy), Kollam, India, 21–23 December 2017, pp. 1–8. IEEE.
14.
MatušůRProkopR (2011) Graphical analysis of robust stability for systems with parametric uncertainty: an overview. Transactions of the Institute of Measurement and Control33(2): 274–290.
15.
MonjeCAVinagreBMFeliuV, et al. (2008) Tuning and auto-tuning of fractional order controllers for industry applications. Control Engineering Practice16: 798–812.
16.
NagrathIGopalM (2008) Textbook of Control Systems Engineering. Delhi: New Age International.
17.
NgoHQTTranAS (2018) Using fuzzy logic scheme for automated guided vehicle to track following path under various load. In: 2018 4th international conference on green technology and sustainable development (GTSD), Ho Chi Minh City, Vietnam, 23–24 November 2018, pp. 312–316. IEEE.
18.
OgataKYangY (2002) Modern Control Engineering. India: Prentice-Hall, Vol. 5.
19.
RajamaniR (2006) Vehicle dynamics and control.
20.
SahaSDasSGhoshR, et al. (2010a) Fractional order phase shaper design with bode’s integral for iso-damped control system. ISA Transactions49: 196–206.
21.
SahaSDasSGhoshR, et al. (2010b) Design of a fractional order phase shaper for iso-damped control of a phwr under step-back condition. IEEE Transactions on Nuclear Science57: 1602–1612.
22.
SamuelMMohamadMHusseinM, et al. (2021) Lane keeping maneuvers using proportional integral derivative (pid) and model predictive control (mpc). Journal of Robotics and Control (JRC)2: 78–82.
23.
SamuelMYahyaKAttarH, et al. (2023) Evaluating the performance of fuzzy-pid control for lane recognition and lane-keeping in vehicle simulations. Electronics12: 724.
24.
SinghS (2015) Critical reasons for crashes investigated in the national motor vehicle crash causation survey.
25.
W.H.O (2023) A report on road traffic injuries worldwide.
26.
WuYWangLZhangJ, et al. (2019) Path following control of autonomous ground vehicle based on nonsingular terminal sliding mode and active disturbance rejection control. IEEE Transactions on Vehicular Technology68: 6379–6390.
27.
YaoQTianY (2019) A model predictive controller with longitudinal speed compensation for autonomous vehicle path tracking. Applied Sciences9: 4739.
28.
ZhaoZ-YTomizukaMIsakaS (1993) Fuzzy gain scheduling of pid controllers. IEEE Transactions on Systems, Man, and Cybernetics23: 1392–1398.
