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International Journal of Engineering Technology and Construction, 2022, 3(2); doi: 10.38007/IJETC.2022.030203.

Fuzzy Control in Compensation System of Deep Mine Mining Pipeline Heave Compensation


Qiwen Zhou, Runzhi Jia, Zhansheng Lin and Fucai Pei

Corresponding Author:
Runzhi Jia

China National Chemical Communications Construction Group Co., Ltd, China


The hydraulic lifting type deep-sea multi-metal manganese nodules mining technology is widely used in deep-sea mining as a cutting-edge technology. In the mining process, due to the influence of ocean wind, waves, current and tide, the mining ship is inevitably in the direction of gravity. A significant heave movement occurs on the top. In order to improve the stability, reliability and service life of the mining pipe and the working efficiency and economy of the entire mining system. A set of lift pipe compensation system should be installed between the upper part of the pipe and the mining ship. Based on the above background, the purpose of this paper is to study the application of fuzzy control in the deep sea mining pipe heave compensation system. In this paper, the idea of fuzzy control is proposed for the time-varying, nonlinear and mathematical models of hydraulic system. The system adopts three main control strategies, namely fuzzy control, fuzzy self-tuning PID control and fuzzy PID composite control. The results show that the classical PID control displacement is between -4.5 and 4.5 mm, while the fuzzy self-tuning PID control displacement is between -4.2 and 2.2 mm, which improves the accuracy of PID control. It can be seen in the fuzzy control with different discrete levels that the large discrete level fuzzy control is obviously higher than the precision of the small discrete level control. It can reach -1.7 to 0.5 mm, greatly improving its accuracy. They are both robust to classical PID control and show good results in control. Among them, fuzzy PID composite control is more superior and practical.


Fuzzy Control, Deep Sea Mining, Mining Pipe, Heave Compensation

Cite This Paper

Qiwen Zhou, Runzhi Jia, Zhansheng Lin and Fucai Pei. Fuzzy Control in Compensation System of Deep Mine Mining Pipeline Heave Compensation. International Journal of Engineering Technology and Construction (2022), Vol. 3, Issue 2: 31-45. https://doi.org/10.38007/IJETC.2022.030203.


[1] Dover C L V, Ardron J A, Escobar E, et al. Biodiversity loss from deep-sea mining. Nature Geoscience, 2017, 10(7):464–465. https://doi.org/10.1038/ngeo2983

[2] Spagnoli G, Miedema S A, Herrmann C, et al. Preliminary Design of a Trench Cutter System for Deep-Sea Mining Applications Under Hyperbaric Conditions. IEEE Journal of Oceanic Engineering, 2016, 41(4):930-943. https://doi.org/10.1109/JOE.2015.2497884

[3] Zou Y, Cao B, Xia J. Water Hammer Pressure Of Hydraulic Lifting Pipeline In Deep-Sea Mining Pilot System. Mechanics in Engineering, 2015, 37(5):603-606. DOI: 10.6052/1000-0879-14-389

[4] Sharma R. Environmental Issues of Deep-Sea Mining . Procedia Earth & Planetary Science, 2015, 11:204-211. https://doi.org/10.1007/978-3-030-12696-4

[5] Hailiang X U, Zeng Y, Chen Q, et al. Numerical simulation of particle flow trajectory in slurry pump for deep-sea mining. Zhongnan Daxue Xuebao, 2017, 48(1):84-90. https://en.cnki.com.cn/Article_en/CJFDTOTAL-ZNGD201701012.htm

[6] Yang H, Liu S. Measuring Method of Solid-Liquid Two-Phase Flow in Slurry Pipeline for Deep-Sea Mining. Thalassas An International Journal of Marine Sciences, 2018, 34(1):1-11. https://doi.org/10.1007/s41208-018-0093-y

[7] Xu F, Rao Q, Ma W. Predicting the sinkage of a moving tracked mining vehicle using a new rheological formulation for soft deep-sea sediment. Chinese Journal of Oceanology and Limnology, 2018, 36(2):230-237. https://doi.org/10.1007/s00343-018-6344-1

[8] Chang W, Huang W, Chang W, et al. Robust fuzzy control for continuous perturbed time‐delay affine takagi–sugeno fuzzy models. International Journal of Control Automation & Systems, 2015, 13(6):818-830. https://doi.org/10.1002/asjc.401

[9] Xu B, Sun F, Pan Y, et al. Disturbance Observer Based Composite Learning Fuzzy Control of Nonlinear Systems with Unknown Dead Zone. IEEE Transactions on Systems Man & Cybernetics Systems, 2017, 47(8):1854-1862. https://doi.org/10.1109/TSMC.2016.2562502

[10] Ding D, Liu Z, Yang S, et al. Battery energy storage aid automatic generation control for load frequency control based on fuzzy control. Power System Protection & Control, 2015, 43(8):81-87. http://en.cnki.com.cn/article_en/cjfdtotal-jdqw201508013.htm

[11] Santín I, Pedret C, Vilanova R. Fuzzy Control and Model Predictive Control Configurations for Effluent Violations Removal in Wastewater Treatment Plants. Industrial & Engineering Chemistry Research, 2015, 54(10):2763-2775. https://doi.org/10.1021/ie504079q

[12] Díaz V H, Martínez J F, Martínez N L, et al. Self-Adaptive Strategy Based on Fuzzy Control Systems for Improving Performance in Wireless Sensors Networks. Sensors, 2015, 15(9):24125-24142. https://doi.org/10.3390/s150924125

[13] Park J, Jeong H, Jang I G, et al. Torque Distribution Algorithm for an Independently Driven Electric Vehicle Using a Fuzzy Control Method. Energies, 2015, 8(8):8537-8561. https://doi.org/10.3390/en8088537

[14] Li Z, Xiao S, Ge S S, et al. Constrained Multilegged Robot System Modeling and Fuzzy Control With Uncertain Kinematics and Dynamics Incorporating Foot Force Optimization. IEEE Transactions on Systems Man & Cybernetics Systems, 2015, 46(1):1-15. https://doi.org/10.1109/TSMC.2015.2422267

[15] Anglano C, Canonico M, Guazzone M. FC2Q: exploiting fuzzy control in server consolidation for cloud applications with SLA constraints. Concurrency & Computation Practice & Experience, 2015, 27(17):4491-4514. https://doi.org/10.1002/cpe.3410

[16] Huang L, Zhang Y, Sun X, et al. Design of hydraulic winch heave compensation system for offshore floating drilling. Acta Petrolei Sinica, 2017, 38(9):1091-1098. DO1:10.7623/syxb201709011

[17] Quan W, Liu Y, Zhang A, et al. The nonlinear finite element modeling and performance analysis of the passive heave compensation system for the deep-sea tethered ROVs. Ocean Engineering, 2016, 127:246-257. https://doi.org/10.1016/j.oceaneng.2016.10.011

[18] Wang S H, Chen H Q, Sun Y Q, et al. The principle and kinetic model of a new offshore crane comprehensive compensation system. Journal of Dalian Maritime University, 2015, 41(1):37-41. http://en.cnki.com.cn/Article_en/CJFDTOTAL-DLHS201501009.htm

[19] Marinaki M , Marinakis Y , Stavroulakis G E . Fuzzy control optimized by a Multi-Objective Differential Evolution algorithm for vibration suppression of smart structures. Computers & Structures, 2015, 147:126-137. https://doi.org/10.1016/j.compstruc.2014.09.018

[20] Fuchun S , Yongping P , Badong C , et al. Disturbance Observer Based Composite Learning Fuzzy Control of Nonlinear Systems with Unknown Dead Zone. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017, 47(8):1854-1862. https://doi.org/10.1109/TSMC.2016.2562502

[21] L. Yin, W. Pan, J. Kuang and M. Zhuang, (2019) "Application of Bootstrap-DEA with Fuzzy Computing in Performance Evaluation of Forklift Leasing Supplier," in IEEE Access. https://doi.org/10.1109/ACCESS.2019.2959598

[22] Ngoc Minh Chau, Nguyen Thi Lan, Nguyen Xuan Thao, A New Similarity Measure of Picture Fuzzy Sets And Application in pattern recognition, American Journal of Business and Operations Research, 2020, Vol. 1, No. 1, pp: 5-18. https://doi.org/10.54216/AJBOR.010101

[23] Ahmed N. Al-Masri , Manal Nasir, A Novel Fuzzy Clustering with Metaheuristic based Resource Provisioning Technique in Cloud Environment, Fusion: Practice and Applications, 2021, Vol. 6, No. 1, pp: 08-16. https://doi.org/10.54216/FPA.060102