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Finite-time adaptive composite integral sliding mode control of Stewart parallel robot based on fast finite-time observer

Published online by Cambridge University Press:  28 July 2025

Xingyu Qu ,
Jiasheng Zhai Open the ORCID record for Jiasheng Zhai [Opens in a new window]  and
Chengxiang Qiao
Xingyu Qu
Affiliation:
School of Electrical Engineering, Shenyang University of Technology, Shenyang, China
Jiasheng Zhai*
Affiliation:
School of Electrical Engineering, Shenyang University of Technology, Shenyang, China
Chengxiang Qiao
Affiliation:
School of Electrical Engineering, Shenyang University of Technology, Shenyang, China
*
Corresponding author: Jiasheng Zhai; Email: 1759630769@qq.com
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Abstract

A finite-time adaptive composite integral sliding mode control strategy based on a fast finite-time observer is proposed for trajectory tracking of the Stewart parallel robot, considering unmodeled uncertainties and external disturbances. First, a global finite-time converging sliding mode surface composed of intermediate variables and integral terms is established to eliminate steady-state tracking errors. Next, a fast finite-time extended state observer is designed to compensate for uncertainties and external disturbances, improving the robustness of the control system. Finally, based on this, a finite-time sliding mode control rate is designed. The gain value is adjusted through an adaptive reaching law to reduce sliding mode chattering, and global finite-time convergence of the system is theoretically proven using Lyapunov theory. Experimental verification shows that the proposed control strategy has stronger robustness to uncertainties and external disturbances, faster error convergence, less chattering, and higher stability accuracy.

Keywords

Stewart parallel robotfinite-time observeradaptive sliding modefinite-time convergencetrajectory tracking

Information

Type
Research Article
Information
Robotica , First View , pp. 1 - 20
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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References

Cai, Y., Zheng, S., Liu, W., Qu, Z. and Han, J., “Model analysis and modified control method of ship-mounted Stewart platforms for wave compensation,” IEEE Access 9, 4505–4517 (2020).10.1109/ACCESS.2020.3047063CrossRefGoogle Scholar
Zhou, Y., She, J., Wang, F. and Iwasaki, M., “Disturbance rejection for stewart platform based on integration of equivalent-input-disturbance and sliding-mode control methods,” IEEE/ASME Trans. Mechatron. 28(4), 2364–2374 (2023).10.1109/TMECH.2023.3237135CrossRefGoogle Scholar
Cai, Y., Zheng, S., Liu, W., Qu, Z., Zhu, J. and Han, J., “Adaptive robust dual-loop control scheme of ship-mounted Stewart platforms for wave compensation,” Mech. Mach. Theory 164, 104406 (2021).10.1016/j.mechmachtheory.2021.104406CrossRefGoogle Scholar
Qazani, M. R. C., Asadi, H., Bellmann, T., Perdrammehr, S., Mohamed, S. and Nahavandi, S.. “A New Fuzzy Logic Based Adaptive Motion Cueing Algorithm Using Parallel Simulation-based Motion Platform.” In: 2020 IEEE International Conference on Fuzzy Systems (2020) pp. 1–8.Google Scholar
Cai, Y., Zheng, S., Liu, W., Qu, Z., Zhu, J. and Han, J., “Sliding-mode control of ship-mounted Stewart platforms for wave compensation using velocity feedforward,” Ocean Eng. 236, 109477 (2021).10.1016/j.oceaneng.2021.109477CrossRefGoogle Scholar
Zhu, Q., Shang, H., Lu, X. and Chen, Y., “Adaptive sliding mode tracking control of underwater vehicle-manipulator systems considering dynamic disturbance,” Ocean Eng. 291, 116300 (2024).10.1016/j.oceaneng.2023.116300CrossRefGoogle Scholar
Yan, Y., Liu, P. and Yu, S., “Neural network-based practical fixed-time nonsingular sliding mode tracking control of autonomous surface vehicles under actuator saturation,” Ocean Eng. 306, 118032–112024 (2024).10.1016/j.oceaneng.2024.118032CrossRefGoogle Scholar
Le, D. V. and Ha, C., “Finite-time fault-tolerant control for a stewart platform using sliding mode control with improved reaching law,” IEEE Access 10, 43284–43302 (2022).10.1109/ACCESS.2022.3165091CrossRefGoogle Scholar
Yao, Q., “Adaptive finite-time sliding mode control design for finite-time fault-tolerant trajectory tracking of marine vehicles with input saturation,” J. Frankl. Inst. 357(18), 13593–13619 (2020).10.1016/j.jfranklin.2020.10.015CrossRefGoogle Scholar
Tajrishi, M. A. Z. and Kalat, A. A., “Fast finite time fractional-order robust-adaptive sliding mode control of nonlinear systems with unknown dynamics,” J. Comput. Appl. Math. 438, 115554–112024 (2024).10.1016/j.cam.2023.115554CrossRefGoogle Scholar
Nie, R., Du, W., Li, Z. and He, S., “Improved finite-time sliding mode control for multi-agent systems under fuzzy topologies,” IEEE Trans. Autom. Sci. Eng. 22, 12147–12159 (2025).10.1109/TASE.2025.3539341CrossRefGoogle Scholar
Chalanga, A. and Bandyopadhyay, B., “Smooth integral sliding mode controller for the position control of Stewart platform,” ISA Trans. 58, 543–551 (2015).Google Scholar
Naderolasli, A., Shojaei, K. and Chatraei, A., “Terminal sliding-mode disturbance observer-based finite-time adaptive-neural formation control of autonomous surface vessels under output constraints,” Robotica 41(1), 236–258 (2023).10.1017/S0263574722001266CrossRefGoogle Scholar
Naderolasli, A., “Indirect self-tuning controller for a two degree of freedom tracker model,” Int. J. Veh. Auton. Syst. 16(1), 15–37 (2021).10.1504/IJVAS.2021.118028CrossRefGoogle Scholar
Mishra, P. K. and Jagtap, P., “Approximation-and chattering-free quasi sliding-mode control for unknown systems,” IEEE Trans. Circuits Syst. II: Express Briefs 71(6), 3081–3085 (2024).Google Scholar
Wang, C., “Adaptive terminal sliding-mode synchronization control with chattering elimination for a fractional-order chaotic system,” Fractal Fract. 8(4), 188–2024 (2024).10.3390/fractalfract8040188CrossRefGoogle Scholar
Dong, H., Yang, X. and Basin, M. V., “Practical tracking of permanent magnet linear motor via logarithmic sliding mode control,” IEEE/ASME Trans. Mechatron. 27(5), 4112–4121 (2022).10.1109/TMECH.2022.3142175CrossRefGoogle Scholar
Deshpande, V. S., Mohan, B., Shendge, P. D. and Phadke, S. B., “Disturbance observer based sliding mode control of active suspension systems,” J. Sound Vib. 333(11), 2281–2296 (2014).10.1016/j.jsv.2014.01.023CrossRefGoogle Scholar
Taimoor, M., Lu, X., Shabbir, W. and Sheng, C., “Neural network observer based on fuzzy auxiliary sliding-mode-control for nonlinear systems,” Expert Syst. Appl. 237, 121492–122024 (2024).10.1016/j.eswa.2023.121492CrossRefGoogle Scholar
Shao, K., Zheng, J., Wang, H., Xu, F., Wang, X. and Liang, B., “Recursive sliding mode control with adaptive disturbance observer for a linear motor positioner,” Mech. Syst. Signal Process. 146, 107014 (2021).10.1016/j.ymssp.2020.107014CrossRefGoogle Scholar
Xiong, S., Wang, W., Liu, X., Chen, Z. and Wang, S., “A novel extended state observer,” ISA Trans. 58, 309–317 (2015).10.1016/j.isatra.2015.07.012CrossRefGoogle Scholar
Dasgupta, B. and Mruthyunjaya, T. S., “Closed-form dynamic equations of the general Stewart platform through the Newton-Euler approach,” Mech. Mach. Theory 33(7), 993–1012 (1998).10.1016/S0094-114X(97)00087-6CrossRefGoogle Scholar
Bhat, S. P. and Bernstein, D. S., “Geometric homogeneity with applications to finite-time stability,” Math. Control Signals Syst. 17(2), 101–127 (2005).10.1007/s00498-005-0151-xCrossRefGoogle Scholar
Li, B., Hu, Q. and Yang, Y., “Continuous finite-time extended state observer based fault tolerant control for attitude stabilization,” Aerosp. Sci. Technol. 84, 204–213 (2019).10.1016/j.ast.2018.10.006CrossRefGoogle Scholar
Yushu, F., Kun, L. and Jian, F., “Finite time adaptive integral sliding mode control method for spacecraft attitude tracking,” J. Univ. Electron. Sci. Technol. 50(4), 527–534 (2021).Google Scholar
Li, D., Yu, H., Tee, K. P., Wu, Y., Ge, S. S. and Lee, T. H., “On time-synchronized stability and control,” IEEE Trans. Syst. Man Cybern. Syst. 52(4), 2450–2463 (2022).10.1109/TSMC.2021.3050183CrossRefGoogle Scholar
Asif, M., Khan, M. J. and Memon, A. Y., “Integral terminal sliding mode formation control of non-holonomic robots using leader follower approach,” Robotica 35(7), 1473–1487 (2017).10.1017/S0263574716000230CrossRefGoogle Scholar
He, Y., Wu, Y. and Li, W., “Nonlinear extended state observer-based adaptive higher-order sliding mode control for parallel antenna platform with input saturation,” Nonlinear Dyn. 111(17), 16111–16132 (2023).10.1007/s11071-023-08701-6CrossRefGoogle Scholar
Dixon, W. E., de Queiroz, M. S., Zhang, F. and Dawson, D. M., “Tracking control of robot manipulators with bounded torque inputs,” Robotica 17(2), 121–129 (1999).10.1017/S0263574799001228CrossRefGoogle Scholar
Chen, Q., Ming, C. and Qin, Y., “Adaptive neural network-based sliding mode control for trajectory tracking control of cable-driven continuum robots with uncertainties,” Robotica 43(1), 316–331 (2025).10.1017/S0263574724001887CrossRefGoogle Scholar
Xie, L., Yu, X. and Chen, L., “Robust fuzzy sliding mode control and vibration suppression of free-floating flexible-link and flexible-joints space manipulator with external interference and uncertain parameter,” Robotica 40(4), 997–1019 (2022).10.1017/S0263574721000977CrossRefGoogle Scholar
Fang, X., Cheng, R., Cheng, S. and Fan, Y., “Nonsingular fixed-time fault-tolerant sliding mode control of robot manipulator with disturbance observer,” Int. J. Control Autom. Syst. 22(7), 2182–2192 (2024).10.1007/s12555-022-0594-6CrossRefGoogle Scholar
Vo, A. T. and Kang, H. J., “An adaptive terminal sliding mode control for robot manipulators with non-singular terminal sliding surface variables,” IEEE Access 7, 8701–8712 (2018).10.1109/ACCESS.2018.2886222CrossRefGoogle Scholar
Naderolasli, A., Shojaei, K. and Chatraei, A., “Fixed-Time multilayer neural network-based leader-follower formation control of autonomous surface vessels with limited field-of-view sensors and saturated actuators,” Neural Comput. Appl. 36(6), 1–21 (2024).Google Scholar
Naderolasli, A. and Shirzadfar, H., “Different applications of the gyroscope sensors data fusion in distinctive systems: An extended kalman filter approach,” Comput. Sistemas 28(1), 189–196 (2024).Google Scholar