This paper presents the first systematic integration of sliding mode observer (SMO), model predictive control (MPC), Kalman-enhanced PID, and adaptive fuzzy logic for Mecanum-wheeled mobile robots (MWMRs), addressing the critical gap in unified control architectures for omnidirectional mobile systems. The key innovations include: (1) a novel adaptive weighting mechanism β(k) for optimal MPC-PID fusion based on real-time tracking dynamics, (2) fuzzy rule-based parameter adaptation specifically designed for MWMR omnidirectional characteristics, and (3) enhanced SMO with wheel-slippage compensation capability. Mathematical modeling incorporates detailed four-wheel dynamics with motor characteristics, followed by systematic integration design ensuring global stability. Comprehensive MATLAB/Simulink simulations across circular, square, figure-eight, S-curve, and infinity-shaped trajectories demonstrate superior performance: 37.2% improvement in tracking accuracy (RMSE: 0.032 m vs 0.087 m for PID), 25.8% reduction in energy consumption, 47.6% faster settling time (0.43 s vs 0.82 s), and enhanced robustness with disturbance rejection factors of 0.873–0.945 under external forces, parameter uncertainties (±20%), and measurement noise. Validation conducted in MATLAB/Simulink R2024b environment with real robot parameters (4.5 kg, 245.6 × 300 × 515 mm). Key limitations include validation restricted to simulation, performance degradation above 2 m/s velocities, and requirement for real-time computational resources (4.92 ms per iteration). This integration provides a systematic framework for high-precision MWMR control in industrial applications, with immediate applicability to material handling, warehouse automation, and service robotics requiring reliable omnidirectional navigation.
AbbasN.AbbasZ.ZafarS.AhmadN.LiuX.KhanS. S.FosterE. D.LarkinS. (2024). Survey of advanced nonlinear control strategies for UAVs: Integration of sensors and hybrid techniques. Sensors, 24(11), 3286. https://doi.org/10.3390/s24113286
2.
AdăscăliţeiF.DorofteiI. D. (2011). Practical applications for mobile robots based on Mecanum wheels – a systematic survey. The Romanian Review Precision Mechanics, Optics & Mechatronics, 40, 21–29.
3.
Al-MahturiA. (2021). Development of Self-Learning Type-2 Fuzzy Systems for System Identification and Control of Autonomous Systems. Diss. University of New South Wales, Australia.
4.
AntonyshynL.SilveiraJ.GivigiS.MarshallJ. (2023). Multiple mobile robot task and motion planning: A survey. ACM Computing Surveys, 55(10), 1–35. https://doi.org/10.1145/3564696
5.
AraujoH.MousaviM. R.VarshosazM. (2023). Testing, validation, and verification of robotic and autonomous systems: A systematic review. ACM Transactions on Software Engineering and Methodology, 32(2), 1–61. https://doi.org/10.1145/3542945
BernalM.SalaA.LendekZ.GuerraT. M. (2022). Analysis and synthesis of nonlinear control systems. Springer International Publishing.
8.
BharatiK. K.SinghO.AhmadA. U. (2024). Feasibility study of efficient and adaptive interval type-2.0 fuzzy logic controller for hybrid electric vehicle performance optimization.
9.
BhosaleA. (2025). Design and Simulation of Agricultural Omnidirectional Robot Using Semi-Circular Mecanum Double Wheel. MS thesis, Texas A&M University-Kingsville.
10.
BianX.ZhaoW.TangL.ZhaoH.MeiX. (2024). An enhanced particle filtering method leveraging particle swarm optimization for simultaneous localization and mapping in Mobile robots navigating unknown environments. Applied Sciences, 14(20), 9426. https://doi.org/10.3390/app14209426
11.
BjelonicM.KottegeN.BeckerleP. (2016). Proprioceptive control of an over-actuated hexapod robot in unstructured terrain. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2042–2049.
12.
CasauP.FerranteF.SanfeliceR. G. (2024). A Tutorial on Hybrid Feedback Control. 2024 IEEE 63rd Conference on Decision and Control (CDC). IEEE.
13.
ChenG.MengQ.ZhuX.CaoA.DingH.LinY. (2025). A bionic blind-guiding robot based on wheeled-legged foldable structure. Journal of Field Robotics. https://doi.org/10.1002/rob.70018
14.
ChennupatiS. (2025). Scalable cloud architectures for real-time AI: dynamic resource allocation for inference optimization. Journal of Computer Science and Technology Studies, 7(3), 690–700. https://doi.org/10.32996/jcsts.2025.7.3.79
15.
ChoiJ.-K.TianH.HaY.-S. (2023). Object transportation using multiple mobile robots: Coupling and decoupling configuration of an object and robots. Journal of Advanced Marine Engineering and Technology47(6), 360–366. https://doi.org/10.5916/jamet.2023.47.6.360
16.
CorkeP.JachimczykW.PillatR. (2023). Mobile robot vehicles. In Robotics, vision and control: Fundamental algorithms in MATLAB® (pp. 127–160). Springer International Publishing.
17.
DasanayakeP. S.BaranauskasV.DervinisG.BalaseviciusL. (2025). A review of mathematical models in robotics. Applied Sciences, 15(14), 8093. https://doi.org/10.3390/app15148093
18.
EIslamM. R. (2024). Parallel computing architectures for robotic applications: A comprehensive review. arXiv preprint arXiv:2407.01011. https://doi.org/10.48550/arXiv.2407.01011
19.
GonçalvesC.Do Rosário CaladoM.PomboN. (2025). Intelligent trajectory planning for autonomous vehicles via adaptive model predictive control. IEEE Access, 13, 87268–872680. https://doi.org/10.1109/ACCESS.2025.3570609
20.
HameedA. H.Al-SamarraieS. A.HumaidiA. J.SaeedN. (2024). Backstepping-based quasi-sliding mode control and observation for electric vehicle systems: A solution to unmatched load and road perturbations. World Electric Vehicle Journal, 15(9), 419. https://doi.org/10.3390/wevj15090419
21.
HanS.-H.PhamD.-A. (2024). A study on kinematics, dynamics, and fuzzy logic controller design for remotely operated vehicles. Journal of Electrical Engineering & Technology, 19(4), 2585–2596. https://doi.org/10.1007/s42835-023-01714-6
22.
Hernandez-BarraganJ.RiosJ. D.AlanisA. Y.Lopez-FrancoC.Gomez-AvilaJ.Arana-DanielN. (2020). Adaptive single neuron anti-windup PID controller based on the extended Kalman filter algorithm. Electronics, 9(4), 636. https://doi.org/10.3390/electronics9040636
23.
HuT.FuchikamiR.IkenagaT. (2023). Temporal prediction-based temporal iterative tracking and parallel motion estimation for a 1-ms rotation-robust LK-based tracking system. IEEE Transactions on Instrumentation and Measurement, 72, 1–14. https://doi.org/10.1109/TIM.2023.3295456
24.
HuangJ.RiM.WuD.RiS. (2017). Interval type-2 fuzzy logic modeling and control of a mobile two-wheeled inverted pendulum. IEEE Transactions on Fuzzy Systems, 26(4), 2030–2038. https://doi.org/10.1109/TFUZZ.2017.2760283
25.
JeongS.ChwaD. (2020). Sliding-mode-disturbance-observer-based robust tracking control for omnidirectional mobile robots with kinematic and dynamic uncertainties. IEEE/ASME Transactions on Mechatronics, 26(2), 741–752. https://doi.org/10.1109/TMECH.2020.2998506
26.
KarrasG. C.FourlasG. K. (2020). Model predictive fault tolerant control for omni-directional mobile robots. Journal of Intelligent & Robotic Systems, 97(3-4), 635–655. https://doi.org/10.1007/s10846-019-01029-7
27.
KorayemM. H.DehkordiS. F.GhobadiN. (2024). Wheel slippage compensation in mobile manipulators through combined kinematic, dynamic, and sliding mode control. Arabian Journal for Science and Engineering, 49(8), 11565–11585. https://doi.org/10.1007/s13369-024-08718-y
28.
KouvaritakisB.CannonM. (2016). Model predictive control. Springer International Publishing, 38, (13–56), 7.
29.
KumarV. (2024). Integrating fuzzy logic with AI for real-Time efficiency optimization in solar cells: A simulation-Based analysis. Journal of Computational Analysis & Applications, 33(6).
30.
LaibA.TalbiB.KramaA.GharibM. (2022). Hybrid interval type-2 fuzzy PID+ I controller for a multi-DOF oilwell drill-string system. IEEE Access, 10, 67262–67275. https://doi.org/10.1109/ACCESS.2022.3185021
31.
LiR.ShariZ.KadirM. Z. A. A. (2025). A review on multi-objective optimization of building performance-Insights from bibliometric analysis. Heliyon, 11(4), e42480. https://doi.org/10.1016/j.heliyon.2025.e42480
LiX.SunZ.CaoD.LiuD.HeH. (2017). Development of a new integrated local trajectory planning and tracking control framework for autonomous ground vehicles. Mechanical Systems and Signal Processing, 87(Part B), 118–137. https://doi.org/10.1016/j.ymssp.2015.10.021
34.
LianY.WangJ.LiZ.LiuW.HuangL.JiangX. (2025). Residual attention guided vision transformer with acoustic-vibration signal feature fusion for cross-domain fault diagnosis. Advanced Engineering Informatics, 64, 103003. https://doi.org/10.1016/j.aei.2024.103003
35.
LiaoJ.JostM.SchaffnerM.MagnoM.KorbM.BeniniL. (2018). FPGA implementation of a kalman-based motion estimator for levitated nanoparticles. IEEE Transactions on Instrumentation and Measurement, 68(7), 2374–2386. https://doi.org/10.1109/TIM.2018.2879146
36.
LiuY.LiJ.ZhangZ.HuX.ZhangW. (2015). Experimental comparison of five friction models on the same test-bed of the micro stick-slip motion system. Mechanical Sciences, 6(1), 15–28. https://doi.org/10.5194/ms-6-15-2015
37.
LiuZ.ZhangZ.LinW.YuX.BuccellaC.CecatiC. 2024). Disturbance rejection-based motion control of linear motors via integral sliding mode observer. IEEE Transactions on Industrial Electronics, 72(3), 3061–3071. https://doi.org/10.1109/TIE.2024.3440512
38.
LuZ.RenY.LiuC.ChenS.ZhaoY. (2025). Design, analysis, and flexibility evaluation of a double-Mode underactuated coupled-Drive omnidirectional mobile robot. Journal of Field Robotics, 42(7), 3138–3152. https://doi.org/10.1002/rob.22574
39.
MartinsN. A.BertolD. W. (2021). Wheeled mobile robot control: theory, simulation, and experimentation. Vol. 380. Springer Nature.
40.
MesbahA. (2016). Stochastic model predictive control: An overview and perspectives for future research. IEEE Control Systems Magazine, 36(6), 30–44. https://doi.org/10.1109/MCS.2016.2602087
41.
MhmoodA. H.MahyuddinM. N. (2025). H∞ sliding mode control: A recent review of applications and design methods. IEEE Access, 13, 136687–136715. https://doi.org/10.1109/ACCESS.2025.3594707
42.
MondalA.GanguliS. (eds.) (2025). Controller Design for Industrial Applications. John Wiley & Sons.
43.
MoshayediA. J.KhanA. S.ShuxinY.KuanG.JiandongH.SoleimaniM.RaziA. (2022). Review on: The service robot mathematical model. EAI Endorsed Transactions on AI and Robotics, 1(1), 1–19. https://doi.org/10.4108/airo.v2i1.3056
44.
MousaviY.BevanG.KucukdemiralI. B.FekihA. (2023). Observer-based high-order sliding mode control of DFIG-based wind energy conversion systems subjected to sensor faults. IEEE Transactions on Industry Applications, 60(1), 1750–1759. https://doi.org/10.1109/TIA.2023.3317823
45.
PatelH. R.ShahV. A. (2022). Shadowed type-2 fuzzy sets in dynamic parameter adaption in cuckoo search and flower pollination algorithms for optimal design of fuzzy fault-tolerant controllers. Mathematical and Computational Applications, 27(6), 89. https://doi.org/10.3390/mca27060089
46.
PérezC. A.TheodoulisS.SèveF.GoerigL. (2022). Automatic weighting filter tuning for robust flight control law design. IFAC-PapersOnLine, 55(16), 400–405. https://doi.org/10.1016/j.ifacol.2022.09.057
47.
PhamD.-A.AhnJ.-K.HanS.-H. (2024). Application of improved sliding mode and artificial neural networks in robot control. Applied Sciences, 14(12), 5304. https://doi.org/10.3390/app14125304
48.
PhamD.-A.HanS.-H. (2024). Critically leveraging theory for optimal control of quadrotor unmanned aircraft systems. Applied Sciences, 14(6), 2414. https://doi.org/10.3390/app14062414
49.
PhamD.-A.HanS.-H.KongK.-J. (2025). Time-specified adaptive robust control framework for managing nonlinear system uncertainties. Applied Sciences, 15(6), 3226. https://doi.org/10.3390/app15063226
50.
PugiL.FavilliT.BerziL.LocorotondoE.PieriniM. (2020). Brake blending and torque vectoring of road electric vehicles: A flexible approach based on smart torque allocation. International Journal of Electric and Hybrid Vehicles, 12(2), 87–115. https://doi.org/10.1504/IJEHV.2020.106339
51.
SchwenzerM.AyM.BergsT.AbelD. (2021). Review on model predictive control: An engineering perspective. The International Journal of Advanced Manufacturing Technology, 117(5), 1327–1349. https://doi.org/10.1007/s00170-021-07682-3
52.
ShamshiriR. (2025). Investigation of Robust Path Tracking, Collision Avoidance, and Teleoperation for Assisted Navigation of an Agricultural Mobile Robot in Unstructured Environments. Technische Universitaet.
53.
SrivastavaV.SrivastavaS.ChaudharyG.Blanco ValenciaX. P. (2024). Performance improvement and lyapunov stability analysis of nonlinear systems using hybrid optimization techniques. Expert Systems, 41(6), e13140. https://doi.org/10.1111/exsy.13140
54.
TangM.LinS.LuoY. (2024). Mecanum wheel AGV trajectory tracking control based on efficient MPC algorithm. IEEE Access, 12, 13763–13772. https://doi.org/10.1109/ACCESS.2024.3356583
55.
TezelC.BayarG. (2024). Theoretical and experimental investigation of variable contact forces on the rollers of a mecanum wheeled mobile robot. The International Journal of Robotics Research, 43(8), 1208–1227. https://doi.org/10.1177/02783649241228607
56.
TolossaT. D.GunasekaranM.HalderK.VermaH. K.ParswalS. S.JorwalN.Maria JosephF. O.HoteY. V. (2024). Trajectory tracking control of a mobile robot using fuzzy logic controller with optimal parameters. Robotica, 42(8), 2801–2824. https://doi.org/10.1017/S0263574724001140
57.
Van HungV.YoonK.-K.LeeS.-G. (2023). Improved sliding mode observer for FOC control system with discontinuous PWM of sensorless PMSM. Journal of Advanced Marine Engineering and Technology (JAMET), 47(5), 244–251. https://doi.org/10.5916/jamet.2023.47.5.244
WangC.SongB.HuangP.TangC. (2016). Trajectory tracking control for quadrotor robot subject to payload variation and wind gust disturbance. Journal of Intelligent & Robotic Systems, 83(2), 315–333. https://doi.org/10.1007/s10846-016-0333-4
60.
WeiH.ShiY. (2022). MPC-based motion planning and control enables smarter and safer autonomous marine vehicles: perspectives and a tutorial survey. IEEE/CAA Journal of Automatica Sinica, 10(1), 8–24. https://doi.org/10.1109/JAS.2022.106016
61.
YaghoubiE.YaghoubiE.MaghamiM. R.RahebiJ.JahromiM. Z.GhadamiR.YusupovZ. (2025). A systematic review and meta-analysis of model predictive control in microgrids: Moving beyond traditional methods. Processes, 13(7), 2197. https://doi.org/10.3390/pr13072197
62.
YiB.BenderP.BonarensF.StillerC. (2018). Model predictive trajectory planning for automated driving. IEEE Transactions on Intelligent Vehicles, 4(1), 24–38. https://doi.org/10.1109/TIV.2018.2886683
63.
Yunusa-KaltungoA.LabibA. (2021). A hybrid of industrial maintenance decision making grids. Production Planning & Control, 32(5), 397–414. https://doi.org/10.1080/09537287.2020.1741046
64.
ZelchC. (2024). Iterative synthesis of extremal fields for near-Optimal feedback control of robotic systems.
65.
ZhangT.ZhangX. (2023). Distributed model predictive control with particle swarm optimizer for collision-free trajectory tracking of mwmr formation. Actuators, 12(3), 127. https://doi.org/10.3390/act12030127
66.
ZhangX. (2021). Adaptive path planning control of snake-like robot based on reinforcement tracking learning. 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP). IEEE.
67.
ZhaoX.SunY.LiY.JiaN.XuJ. (2024). Applications of machine learning in real-time control systems: a review. Measurement Science and Technology, 36(1). https://doi.org/10.1088/1361-6501/ad8947
