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An integrated control strategy combining fuzzy gain with dynamic feedforward for parallel shipborne stabilisation platforms

Published online by Cambridge University Press:  26 August 2025

Jiayue Zhang Open the ORCID record for Jiayue Zhang [Opens in a new window] ,
Hengchun Cui ,
Jun Wu Open the ORCID record for Jun Wu [Opens in a new window]  and
Yanling Tian
Jiayue Zhang
Affiliation:
Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China Beijing Key Laboratory of Transformative High-end Manufacturing Equipment and Technology, Tsinghua University, Beijing, 100084, China
Hengchun Cui
Affiliation:
Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China Beijing Key Laboratory of Transformative High-end Manufacturing Equipment and Technology, Tsinghua University, Beijing, 100084, China
Jun Wu*
Affiliation:
Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China Beijing Key Laboratory of Transformative High-end Manufacturing Equipment and Technology, Tsinghua University, Beijing, 100084, China
Yanling Tian
Affiliation:
School of Engineering, University of Warwick, Coventry, CV4 7AL, UK
*
Corresponding author. Jun Wu; Email: jhwu@mail.tsinghua.edu.cn
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Abstract

The control of shipborne stabilisation platforms is challenging due to the effects of platform dynamic characteristics and unpredictable wave disturbances in operational environments. This paper proposes an integrated control strategy that combines dynamic feedforward and fuzzy gain control. Based on the derived dynamic model of the shipborne stabilisation platform, a dynamic feedforward controller is designed to mitigate the effects of platform dynamics on motion accuracy. In the fuzzy gain control design, scaling modules are proposed to enhance the fuzzy controller’s adaptability to varying operating conditions and unpredictable wave disturbances. The motion of the stabilisation platform is simulated by taking the motion of the lower platform calculated based on the wave fluctuations in marine environments as the input. The prototype experiment is conducted by using a large-scale parallel mechanism to simulate the wave environments. Simulation and experimental results indicate that the proposed control strategy achieves real-time disturbance compensation without precise mathematical modelling or pre-training, and demonstrates good adaptability.

Keywords

shipborne stabilisation platformcontrol strategycontrol of robotic systemsfuzzy gain controldynamic feedforward control

Information

Type
Research Article
Information
Robotica , First View , pp. 1 - 17
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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References

Tian, W., Zhang, X., Lv, D., Wang, L. and Liu, Q., “Sliding mode control strategy of 3-UPS/S shipborne stable platform with LSTM neural network prediction,” Ocean Eng. 265, 112497 (2022).10.1016/j.oceaneng.2022.112497CrossRefGoogle Scholar
Zhang, B. and Shang, W., “Kinematic Control of a 3-DOF Parallel Stabilization Platform,” Proceedings of the 33rd Chinese Control Conference (Nanjing, China, 2014) pp. 8287–8292.10.1109/ChiCC.2014.6896389CrossRefGoogle Scholar
Liu, W., Du, J., Li, J. and Li, Z., “Stabilization control of 3-DOF parallel vessel-borne platform with dynamic uncertainties and unknown disturbances,” Appl. Ocean Res. 126, 103271 (2022).10.1016/j.apor.2022.103271CrossRefGoogle Scholar
Chang, S., Yang, D., Cao, J. and Li, X., “An extend sliding mode disturbance observer for optical inertial platform line-of-sight stabilized control,” Machines 12(12), 849 (2024).10.3390/machines12120849CrossRefGoogle Scholar
Guo, L., Tan, Z., Yang, L. and Zhang, L., “Application of NN-based Backstepping Adaptive Controller for Stabilized Platform of Shipborne Weapons,”2nd International Conference on Advanced Computer Control (Shenyang, China, 2010) pp. 271–276.10.1109/ICACC.2010.5487003CrossRefGoogle Scholar
Guo, L. and Tan, Z., “Application of a Sliding Mode Variable Structure Controller for Stabilized Platform of Shipborne Weapons,” 2nd IEEE International Conference on Information Management and Engineering (Chengdu, China, 2010) pp. 65–68.10.1109/ICIME.2010.5477594CrossRefGoogle Scholar
Bi, F., Ma, T., Wang, X., Yang, X. and Lv, Z., “Research on vibration control of seating system platform based on the cubic Stewart parallel mechanism,” IEEE Access 7, 155637–155649 (2019).10.1109/ACCESS.2019.2948785CrossRefGoogle Scholar
Liu, Y., Yuan, H., Xiao, Z. and Xiao, C., “An offshore self-stabilized system based on motion prediction and compensation control,” J. Mar. Sci. Eng. 11(4), 745 (2023).10.3390/jmse11040745CrossRefGoogle Scholar
Wang, L., Jiang, J. and Wang, M., “Research on MPC Control of a 6 DOF Stewart Platform,” IEEE International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale (3M-NANO) (Tianjin, China, 2022) pp. 65–69.10.1109/3M-NANO56083.2022.9941574CrossRefGoogle Scholar
Qiang, H., Jin, S., Feng, X., Xue, D. and Zhang, L., “Model predictive control of a shipborne hydraulic parallel stabilized platform based on ship motion prediction,” IEEE Access 8, 181880–181892 (2020).10.1109/ACCESS.2020.2992458CrossRefGoogle Scholar
Ramesh, P., Chanlanga, A. and Bandyopadhyay, B., “Smooth integral sliding mode controller for the position control of Stewart platform,” ISA Trans. 58, 543–551 (2015).Google Scholar
Wang, X. and Liu, C., “Nonsingular Fast Terminal Sliding Mode Control of Stewart Parallel Robot,” Proceedings of 2023 Chinese Intelligent Systems Conference (CISC 2023), Lecture Notes in Electrical Engineering, vol. 1089 (Springer, Singapore, 2023).10.1007/978-981-99-6847-3_61CrossRefGoogle Scholar
Chi, W., Ma, H., Wang, C. and Zhao, T., “Research on control of Stewart platform integrating small attitude maneuver and vibration isolation for high-precision payloads on spacecraft,” Aerospace 8(11), 333 (2021).10.3390/aerospace8110333CrossRefGoogle Scholar
Safeena, M., Ramesh, P. and Jiji, K., “Control of Stewart platform using adaptive smooth integral sliding mode algorithm,” ISA Trans. 156, 99–108 (2025).Google Scholar
Amit, S. and Hamad, K., “Modeling simulation & control of 6-DOF parallel manipulator using PID controller and compensator,” IFAC Proc. 47(1), 421–428 (2014).Google Scholar
Song, X., Zhao, Y., Chen, C., Zhang, L. and Lu, X., “A PD computed torque control method with online self-gain tuning for a 3UPS-PS parallel robot,” Robotica 39(9), 1575–1587 (2021).10.1017/S0263574720001368CrossRefGoogle Scholar
Pak, Y.-J., Kong, Y.-S. and Ri, J.-S., “Robust PID optimal tuning of a Delta parallel robot based on a hybrid optimization algorithm of particle swarm optimization and differential evolution,” Robotica 41(4), 1159–1178 (2023).10.1017/S0263574722001606CrossRefGoogle Scholar
Ghanifar, M., Kamzan, M. and Tayefi, M., “Different intelligent methods for coefficient tuning of quadrotor feedback-linearization controller,” J. Aerospace Sci. Technol. 16(1), 56–65 (2023).Google Scholar
Mojtaba, E., Mahdi, E. and Hossein, K., “Neuro-fuzzy adaptive control of a revolute Stewart platform carrying payloads of unknown inertia,” Robotica 33(9), 2001–2024 (2015).Google Scholar
Khanbabayi, E. and Noorani, M., “Design computed torque control for Stewart platform with uncertainty to the rehabilitation of patients with leg disabilities,” Comput. Methods Biomech. Biomed. Eng. 27(8), 1028–1041 (2023).10.1080/10255842.2023.2222863CrossRefGoogle Scholar
Fijany, A. and Fried, G., “Novel Algorithms for Computation of Inverse Kinematics and Inverse Dynamics of Gough-Stewart Platform,” IEEE/RSJ International Conference on Intelligent Robots and Systems (Vilamoura-Algarve, Portugal, 2012) pp. 1573–1580.10.1109/IROS.2012.6386014CrossRefGoogle Scholar
Gosselin, C. and Merlet, J., “Parallel robots: Architecture, modeling, and design,” in M. H. Ang, O. Khatib, & B. Siciliano (Eds.), Springer, Berlin Heidelberg, Encycl. Rob. 1–6 (2020).Google Scholar
Wang, J., Wu, J., Wang, L. and You, Z., “Dynamic feed-forward control of a parallel kinematic machine,” Mechatronics 19(3), 313–324 (2009).10.1016/j.mechatronics.2008.11.004CrossRefGoogle Scholar
Wu, J., Wang, J., Li, T., Wang, L. and Guan, L., “Dynamic dexterity of a planar 2-DOF parallel manipulator in a hybrid machine tool,” Robotica 26(1), 93–98 (2008).10.1017/S0263574707003621CrossRefGoogle Scholar
Wang, J., Wu, J., Li, T. and Liu, X., “Workspace and singularity analysis of a 3-DOF planar parallel manipulator with actuation redundancy,” Robotica 27(1), 51–57 (2009).10.1017/S0263574708004517CrossRefGoogle Scholar
Wang, M., Chen, Q., Liu, H., Huang, T., Feng, H. and Tian, W., “Evaluation of the kinematic performance of a 3-RRS parallel mechanism,” Robotica 39(4), 606–617 (2021).10.1017/S0263574720000612CrossRefGoogle Scholar
Wang, Y., Qiu, C., He, X., Wu, J., Xu, Y. and Zhao, Y., “Type synthesis of a new parallel mechanism with complete separation of constraints and drives,” Robotica 42(9), 3174–3189 (2024).10.1017/S0263574724001346CrossRefGoogle Scholar
Li, R., Li, T., Bai, W. and Du, X., “An adaptive neural network approach for ship roll stabilization via fin control,” Neurocomputing 173(3), 953–957 (2016).10.1016/j.neucom.2015.08.050CrossRefGoogle Scholar
Luo, W., Hu, B. and Li, T., “Neural network based fin control for ship roll stabilization with guaranteed robustness,” Neurocomputing 230, 210–218 (2017).10.1016/j.neucom.2016.12.023CrossRefGoogle Scholar
Wang, H. and Wang, D., “An Improved Primal-Dual Genetic Algorithm for Optimization in Dynamic Environments,” International Conference on Neural Information Processing (Berlin, Heidelberg, 2006) pp. 836–844.10.1007/11893295_92CrossRefGoogle Scholar
Dilimulati, B. and Bruha, I., “Genetic algorithms in a dynamically changing environment,” WIT Trans. Inf. Commun. Technol. 38, 65–73 (2007).Google Scholar
Tang, H., Zhao, X., Zhang, J. and Peng, X., “Modeling and Prediction of Disturbing Force and Moment Based on Harmonic Analysis for Ship Cross Direction,” ISIST’2004 (2004) p. 1.Google Scholar