As a key technical foundation for tracked unmanned ground vehicles (UGVs) to autonomously execute missions in high-risk environments, path-following control is a core prerequisite for achieving autonomous driving capability. To address the lack of research on path-following for a new configuration of the Distributed Tracked Unmanned Vehicle (DTUV), this paper first establishes the dynamics and kinematics models of the DTUV, including the track model, motor model, and whole-vehicle model. Subsequently, a hierarchical path-following controller is designed, with a model predictive control (MPC) modified by proportional integral derivative (PID) control as the upper layer and a torque-distribution algorithm based on adaptive weight particle swarm optimization (AWPSO) as the lower layer, namely the MPC-PID-PSO controller. Finally, the effectiveness of the proposed controller under three typical paths is verified through co-simulation. The results show that the proposed control strategy algorithm has better lateral error performance than other control algorithms, with a 2.7%–53.8% lateral error reduction. Meanwhile, it also has certain energy-saving effects, with a 5.5%–16.5% reduction in energy consumption. The research findings of this paper can provide a theoretical basis and control strategy guidance for the engineering practice of the DTUV and other UGVs.
ErsüCPetlenkovEJansonK. A systematic review of cutting-edge radar technologies: applications for unmanned ground vehicles (UGVs). Sensors (Basel)2024; 24(23): 7807. https://doi.org/10.3390/s24237807
2.
ConceicaoAGSantosTLRibeiroTT. Soft trajectory tracking control for wheeled unmanned ground vehicles under uncertain slippage conditions. J Franklin Inst2025; 362(9): 107706. https://doi.org/10.1016/j.jfranklin.2025.107706
3.
XuXChenGGaoX, et al. Stability and energy-saving coordinated control strategy of six-wheel independent drive unmanned ground vehicle. ISA Trans2023; 143: 692–706. https://doi.org/10.1016/j.isatra.2023.10.001
4.
ÇeliksözDKılıçV. Series-hybrid powertrains: advancing mobility control in electric tracked vehicle technology. World Electr Veh J2024; 15(2): 47. https://doi.org/10.3390/wevj15020047
5.
HryciówZRybakP. Numerical research of the high-speed military vehicle track. AIP Conf Proc2019; 2078(1): 020029. https://doi.org/10.1063/1.5092032
6.
KellerTArvidssonJ. A model for prediction of vertical stress distribution near the soil surface below rubber-tracked undercarriage systems fitted on agricultural vehicles. Soil Tillage Res2016; 155: 116–123. https://doi.org/10.1016/j.still.2015.07.014
7.
GuoJDaiZLiuM, et al. Distributed drive electric vehicle handling stability coordination control framework based on adaptive model predictive control. Sensors (Basel)2024; 24(15): 4811. https://doi.org/10.3390/s24154811
8.
LiuBXuXPanD, et al. Drag reduction design and research of high-speed amphibious vehicles deformable track wheels. Ships Offshore Struct2023; 18(7): 970–979. https://doi.org/10.1080/17445302.2022.2093032
9.
XuHXuXXuL, et al. Sailing resistance reduction and speed raising for amphibious vehicles using novel configuration. Sci Sin Technol2023; 53(8): 1272–1283. https://doi.org/10.1360/SST-2023-0049
10.
WangWHChenYYLiYF, et al. Tractive performance evaluation for unmanned amphibious traction vehicles on beach areas. Proc Inst Mech Eng Part D: J Automob Eng. Epub ahead of print 30May2025. https://doi.org/10.1177/09544070251337210
WongJYChiangCF. A general theory for skid steering of tracked vehicles on firm ground. Proc Inst Mech Eng Part D: J Automob Eng2001; 215(3): 343–355. https://doi.org/10.1243/0954407011525683
13.
JanarthananBPadmanabhanCSujathaC. Lateral dynamics of single unit skid-steered tracked vehicle. Int J Automot Technol2011; 12(6): 865–875. https://doi.org/10.1007/s12239-011-0099-4
14.
TotaAGalvagnoEVelardocchiaM. Analytical study on the cornering behavior of an articulated tracked vehicle. Machines2021; 9(2): 1–23. https://doi.org/10.3390/machines9020038
15.
FangYZhangYShangY, et al. Center-point steering analysis of tracked omni-vehicles based on skid conditions. Mech Sci2021; 12(1): 511–527. https://doi.org/10.5194/ms-12-511-2021
16.
MilićevićSVBlagojevićIA. Theoretical model of high-speed tracked vehicle general motion. FME Trans2023; 51(3): 449–456. https://doi.org/10.5937/fme2303449M
17.
DimauroLVenturiniSTotaA, et al. High-speed tracked vehicle model order reduction for static and dynamic simulations. Def Technol2024; 38(8): 89–110. https://doi.org/10.1016/j.dt.2024.01.006
18.
LiNLiuXChenH, et al. Modeling and analysis of the turning performance of an articulated tracked vehicle that considers the inter-unit coupling forces. Machines2025; 13(2): 118. https://doi.org/10.3390/machines13020118
19.
ShenYZhaoYDengH, et al. Coordinated control of stability and economy of distributed drive electric vehicle based on Lyapunov adaptive theory. Proc Inst Mech Eng Part D: J Automob Eng2024; 238(6): 1535–1549. https://doi.org/10.1177/09544070221147081
20.
ChenZPanSYuK, et al. A composite fixed-time sliding mode control scheme for Unmanned Ground Vehicles affected by external disturbances. ISA Trans2025; 156: 524–534. https://doi.org/10.1016/j.isatra.2024.11.003
21.
JiYHLiuYC. Force allocation control for distributed drive electric vehicles under split-friction regions and actuator faults. ISA Trans2022; 129: 398–412. https://doi.org/10.1016/j.isatra.2022.03.001
22.
ZhaiLWangCHouY, et al. Two-level optimal torque distribution for handling stability control of a four hub-motor independent-drive electric vehicle under various adhesion conditions. Proc Inst Mech Eng Part D: J Automob Eng2022; 237(2): 544–559. https://doi.org/10.1177/09544070221075508
23.
LiangJFengJFangZ, et al. An energy-oriented torque-vector control framework for distributed drive electric vehicles. IEEE Trans Transp Electrif2023; 9(3): 4014–4031. https://doi.org/10.1109/TTE.2022.3231933
24.
YanYZhangCXueP, et al. Research on hierarchical motion control of corner module configuration intelligent electric vehicle. Chin J Mech Eng2025; 38(1): 1–15. https://doi.org/10.1186/s10033-025-01190-1
25.
LvHWangXXieB, et al. A hierarchical cooperative control strategy for in-situ steering of distributed drive electric vehicle. IEEE Trans Transp Electrif2025; 11(3): 1. https://doi.org/10.1109/TTE.2025.3540866
26.
GuoNLiuJLiJ, et al. Handling-stability control for distributed drive electric vehicles via Lyapunov-based nonlinear MPC algorithm. IEEE Trans Transp Electrif2025; 11(2): 6615–6628. https://doi.org/10.1109/TTE.2024.3513438
27.
ChenXJiangRZhaoW, et al. Tube-based cooperative robust control for distributed drive electric vehicles suffering actuator faults. IEEE Trans Transp Electrif2025; 11(1): 3502–3513. https://doi.org/10.1109/TTE.2024.3442572
28.
LiDZhaoYLinF, et al. Mixed-objective robust hierarchical lateral control of autonomous distributed drive electric vehicles considering parametric uncertainties and energy loss. J Franklin Inst2025; 362(8): 107693. https://doi.org/10.1016/j.jfranklin.2025.107693
29.
WeiCLiYOuyangY, et al. Deep reinforcement learning with heuristic corrections for UGV navigation. J Intell Robot Syst2023; 109(1): 18. https://doi.org/10.1007/s10846-023-01950-y
30.
DongYCamciEKayacanE. Faster RRT-based nonholonomic path planning in 2D building environments using skeleton-constrained path biasing. J Intell Robot Syst2018; 89(3): 387–401. https://doi.org/10.1007/s10846-017-0567-9
31.
MałekKDybałaJKordeckiA, et al. OffRoadSynth open dataset for semantic segmentation using synthetic-data-based weight initialization for autonomous UGV in off-road environments. J Intell Robot Syst2024; 110(2): 1–18. https://doi.org/10.1007/s10846-024-02114-2
32.
LuSJiangYZhangL, et al. Adaptive differential steering strategy for distributed driving unmanned ground vehicle with variable configurations based on modified localized modelling sliding mode control. ISA Trans2024; 151: 391–408. https://doi.org/10.1016/j.isatra.2024.05.045
33.
SekbanHTBasciA. Designing new model-based adaptive sliding mode controllers for trajectory tracking control of an unmanned ground vehicle. IEEE Access2023; 11: 101387–101397. https://doi.org/10.1109/ACCESS.2023.3313942
34.
JiangYXuXZhangL, et al. Model free predictive path tracking control of variable-configuration unmanned ground vehicle. ISA Trans2022; 129: 485–494. https://doi.org/10.1016/j.isatra.2022.01.026
35.
ChenTZhaoRChenJ, et al. Data-driven active disturbance rejection control of plant-protection unmanned ground vehicle prototype: a fuzzy indirect iterative learning approach. IEEE CAA J Autom Sin2024; 11(8): 1–3. https://doi.org/10.1109/JAS.2023.124158
36.
WangHHuCCuiW, et al. Multi-objective comprehensive control of trajectory tracking for four-in-wheel-motor drive electric vehicle with differential steering. IEEE Access2021; 9: 62137–62154. https://doi.org/10.1109/ACCESS.2021.3074215
37.
ChenWLiuFZhaoH. Trajectory-tracking control of unmanned vehicles based on adaptive variable parameter MPC. Appl Sci2024; 14(16): 7285. https://doi.org/10.3390/app14167285
38.
SongJTaoGZangZ, et al. Isolating trajectory tracking from motion control: a model predictive control and robust control framework for unmanned ground vehicles. IEEE Robot Autom Lett2023; 8(3): 1699–1706. https://doi.org/10.1109/LRA.2023.3242151
39.
WangXWangYSunQ, et al. Adaptive robust control of unmanned tracked vehicles for trajectory tracking based on constraint modeling and analysis. Nonlinear Dyn2024; 112(11): 9117–9135. https://doi.org/10.1007/s11071-024-09514-x
40.
YanXWangSHeY, et al. Autonomous tracked vehicle trajectory tracking control based on disturbance observation and sliding mode control. Actuators2025; 14(2): 51. https://doi.org/10.3390/act14020051
41.
WangSGuoJMaoY, et al. Research on the model predictive trajectory tracking control of unmanned ground tracked vehicles. Drones2023; 7(8): 496. https://doi.org/10.3390/drones7080496
