When an automated guided vehicle follows a desired trajectory, the precision of the trajectory tracking results may be inevitably affected by unknown-but-bounded noises, such as the ground friction, battery fluctuations, and other factors. Thus, this paper investigates the trajectory tracking problem of automated guided vehicles subject to such noises. First, a proximal policy optimization algorithm based on a set-membership estimator is proposed to realize the trajectory tracking of automated guided vehicles under unknown-but-bounded noises. Then, a discrete reward based on thresholds of the trajectory tracking error is proposed for the reward function design, which guides the policy optimization algorithm to limit the tracking error within a certain range in order to improve the accuracy of trajectory tracking. Finally, simulation experiments demonstrate the effectiveness and superiority of the proposed method.
BaltesJChristmannGSaeedvandS (2023) A deep reinforcement learning algorithm to control a two-wheeled scooter with a humanoid robot. Engineering Applications of Artificial Intelligence126: 106941.
2.
ChenZZhaoHShuX, et al. (2021) Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H-Infinity filter. Energy228: 120630.
3.
CuiZGuanWLuoW, et al. (2023) Intelligent navigation method for multiple marine autonomous surface ships based on improved PPO algorithm. Ocean Engineering287: 115783.
4.
ElsisiMAltiusMSuSF, et al. (2023) Robust Kalman filter for position estimation of automated guided vehicles under cyberattacks. IEEE Transactions on Instrumentation and Measurement72: 1–12.
5.
EysenbachBLevineS (2022) Maximum entropy RL (provably) solves some robust RL problems. arXiv 2103.06257. Available at: https://arxiv.org/abs/2103.06257
6.
FuWLiuYZhangX (2023) Research on accurate motion trajectory control method of four-wheel steering AGV based on Stanley-PID control. Sensors23(16): 7219.
7.
GoodfellowIJPouget-AbadieJMirzaM, et al. (2014) Generative adversarial nets. In: Proceedings of the 28th international conference on neural information processing systems - vol. 2, Montreal, QC, Canada, 8–13 December, NIPS’14, pp. 2672–2680. Cambridge, MA: MIT Press.
8.
GuoHRenZLaiJ, et al. (2023) Optimal navigation for AGVs: A soft actor–critic-based reinforcement learning approach with composite auxiliary rewards. Engineering Applications of Artificial Intelligence124: 106613.
9.
HwangCLYangCCHungJY (2018) Path tracking of an autonomous ground vehicle with different payloads by hierarchical improved fuzzy dynamic sliding-mode control. IEEE Transactions on Fuzzy Systems26(2): 899–914.
10.
LiBGongWYangY, et al. (2022) Appointed fixed time observer-based sliding mode control for a quadrotor UAV under external disturbances. IEEE Transactions on Aerospace and Electronic Systems58(1): 290–303.
11.
LiBLiuHAhnCK, et al. (2024a) Optimized intelligent tracking control for a quadrotor unmanned aerial vehicle with actuator failures. Aerospace Science and Technology144: 108803.
12.
LiJYangCFangJ, et al. (2025) A fault-tolerant trajectory tracking strategy of four-wheel independent drive electric vehicles using super-twisting sliding model control. IEEE Transactions on Transportation Electrification11(1): 3907–3917.
LiangZHuJZhangY, et al. (2024) Set-membership filtering based model predictive control for trajectory tracking of automated guided vehicles. International Journal of Adaptive Control and Signal Processing38(12): 3819–3829.
15.
LiangZWangZZhaoJ, et al. (2023) Fixed-time prescribed performance path-following control for autonomous vehicle with complete unknown parameters. IEEE Transactions on Industrial Electronics70(8): 8426–8436.
16.
LiangZZhaoJLiuB, et al. (2022) Velocity-based path following control for autonomous vehicles to avoid exceeding road friction limits using sliding mode method. IEEE Transactions on Intelligent Transportation Systems23(3): 1947–1958.
17.
LiuHLiBAhnCK (2025a) Asymptotically stable-learning-based formation control for multiquadrotor UAVs. IEEE Internet of Things Journal12(13): 24985–24995.
18.
LiuJZhongYHuS, et al. (2025b) Maximum entropy heterogeneous-agent reinforcement learning. Available at: https://arxiv.org/abs/2306.10715
19.
LiuXWangGChenK (2021) Nonlinear model predictive tracking control with C/GMRES method for heavy-duty AGVs. IEEE Transactions on Vehicular Technology70(12): 12567–12580.
20.
LuCKubaJLetcherA, et al. (2022) Discovered policy optimisation. Advances in Neural Information Processing Systems35: 16455–16468.
21.
MiyatoTKataokaTKoyamaM, et al. (2018) Spectral normalization for generative adversarial networks. Available at: https://arxiv.org/abs/1802.05957
22.
SchulmanJWolskiFDhariwalP, et al. (2017) Proximal policy optimization algorithms. Available at: https://arxiv.org/abs/1707.06347
23.
ShiQZhangH (2022) Road-curvature-range-dependent path following controller design for autonomous ground vehicles subject to stochastic delays. IEEE Transactions on Intelligent Transportation Systems23(10): 17440–17450.
24.
ShuXChenZShenJ, et al. (2023) State of charge estimation for lithium-ion battery based on hybrid compensation modeling and adaptive H-Infinity filter. IEEE Transactions on Transportation Electrification9(1): 945–957.
WangYPuigVCembranoG (2018) Set-membership approach and Kalman observer based on zonotopes for discrete-time descriptor systems. Automatica93: 435–443.
27.
WangZLiangZDingZ (2025) Fixed-time approach for automated ground vehicles path following subject to prescribed error constraints and completely unknown steering dead zone. IEEE Transactions on Industrial Informatics21(1): 238–247.
28.
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(7): 6379–6390.
29.
YangFLiY (2009) Set-membership filtering for systems with sensor saturation. Automatica45(8): 1896–1902.
30.
YangHZhangYGuW, et al. (2022) Set-membership filtering for automatic guided vehicles with unknown-but-bounded noises. Transactions of the Institute of Measurement and Control44(3): 716–725.
31.
YangSWangKWangW, et al. (2025) Dual-attention proximal policy optimization for efficient autonomous navigation in narrow channels using deep reinforcement learning. Ocean Engineering326: 120707.
32.
ZhaoJLiRZhengX, et al. (2025) Constrained fractional-order model predictive control for robust path following of FWID-AGVs with asymptotic prescribed performance. IEEE Transactions on Vehicular Technology74(2): 2692–2705.
33.
ZhaoWWeiHAiQ, et al. (2024) Real-time model predictive control of path-following for autonomous vehicles towards model mismatch and uncertainty. Control Engineering Practice153: 106126.
