-
CiteScore
-
Impact Factor
IECE Transactions on Intelligent Unmanned Systems, 2024, Volume 1, Issue 1: 31-43

Research Article | 21 July 2024
1 Changsha Institute of Mining Research, Changsha 410000, China
2 Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
* Corresponding author: Yuheng Chen, email: [email protected]
Received: 18 May 2024, Accepted: 09 July 2024, Published: 21 July 2024  

Abstract
This paper explores the effectiveness of Model Predictive Control (MPC) for trajectory tracking in autonomous deep-sea tracked mining vehicles operating within polymetallic nodule mining environments, considering model uncertainties and external disturbances. Traditional applications of MPC in autonomous vehicle trajectory tracking, which typically rely on kinematic models under minimal external disturbance, often fail when faced with model inaccuracies and external disruptions. To address these challenges, we propose an MPC-based trajectory tracking algorithm that includes a speed correction controller for the drive wheel. This controller, developed through experimental data fitting, aims to mitigate issues such as vehicle body subsidence and track slippage. Tracking accuracy, particularly in curve navigation, is further enhanced through the use of Kalman Filtering (KF) and Adaptive Kalman Filtering (AKF) to counteract external disturbances. Moreover, we introduce an obstacle avoidance strategy utilizing a tri-circular arc trajectory with uniform curvature for path re-planning. This strategy effectively addresses dead zones and physical obstructions encountered during operation. The superiority of our approach compared to conventional Nonlinear MPC (NMPC) is demonstrated through extensive Simulink and Recurdyn co-simulations.

Graphical Abstract
Model Predictive Control for Enhanced Trajectory Tracking of Autonomous Deep-Sea Tracked Mining Vehicles

Keywords
Deep-sea tracked mining vehicle
Trajectory tracking
Model predictive control
Kalman filter
Path planning

References

[1]Lesage, M., Juliani, C., & Ellefmo, S. L. (2018). Economic block model development for mining seafloor massive sulfides. Minerals, 8(10), 468.

[2]Volkmann, S. E., Kuhn, T., & Lehnen, F. (2018). A comprehensive approach for a techno-economic assessment of nodule mining in the deep sea. Mineral economics, 31, 319-336.

[3] Dai Y, Liu S. An integrated dynamic model of ocean mining system and fast simulation of its longitudinal reciprocating motion[J]. China Ocean Engineering, 2013, 27(2): 231-244.

[4] Dai Y, Liu S. Theoretical design and dynamic simulation of new mining paths of tracked miner on deep seafloor[J]. Journal of Central South University, 2013, 20(4): 918-923.

[5] Dai, Y., Liu, S., & Li, L.(2010). Dynamic analysis of the seafloor pilot miner based on single-body vehicle model and discretized track-terrain interaction model. China ocean engineering, 24(1), 145-160.

[6] Li, J., Liu, S., & Dai, Y. (2017). Effect of grouser height on tractive performance of tracked mining vehicle. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 39, 2459-2466.

[7] Dai, Y., & Liu, S. J. (2013). Theoretical design and dynamic simulation of new mining paths of tracked miner on deep seafloor. Journal of Central South University, 20(4), 918-923.

[8] Li, L., Zheng, Z., & Chen, M. (2014) Point stabilization of seabed mining vehicle based on Lyapunov theory, Journal of Central South University, vol. 45, no. 08, pp. 2624-2628, 2014.

[9] Li, L., & Zou, Y. H. (2012). Tracking moving path of seabed mining vehicle based on theory of variable universe fuzzy control. Journal of Central South University, 43(02), 489-496.

[10] Gan, W., Zhu, D., Hu, Z., Shi, X., Yang, L., & Chen, Y. (2019). Model predictive adaptive constraint tracking control for underwater vehicles. IEEE Transactions on Industrial Electronics, 67(9), 7829-7840.

[11] Shen, C., Shi, Y., & Buckham, B. (2017). Trajectory tracking control of an autonomous underwater vehicle using Lyapunov-based model predictive control. IEEE Transactions on Industrial Electronics, 65(7), 5796-5805.

[12] Chen, Y., Xie, X., Zhang, T., Bai, J., & Hou, M. (2020). A deep residual compensation extreme learning machine and applications. Journal of Forecasting, 39(6), 986-999.

[13] Chen, Y., Yi, C., Xie, X., Hou, M., & Cheng, Y. (2020). Solution of ruin probability for continuous time model based on block trigonometric exponential neural network. Symmetry, 12(6), 876.

[14] Saback, R. M., Conceicao, A. G. S., Santos, T. L. M., Albiez, J., & Reis, M. (2019). Nonlinear model predictive control applied to an autonomous underwater vehicle. IEEE Journal of Oceanic Engineering, 45(3), 799-812.

[15] Zhang, B., Sun, X., Liu, S., & Deng, X. (2020). Adaptive model predictive control with extended state observer for multi-UAV formation flight. International Journal of Adaptive Control and Signal Processing, 34(10), 1341-1358.

[16] Song, X., Shao, Y., & Qu, Z. (2019). A vehicle trajectory tracking method with a time-varying model based on the model predictive control. IEEE Access, 8, 16573-16583.

[17] Wu, H., Si, Z., & Li, Z. (2020). Trajectory tracking control for four-wheel independent drive intelligent vehicle based on model predictive control. IEEE Access, 8, 73071-73081.

[18] Li, S., Li, Z., Yu, Z., Zhang, B., & Zhang, N. (2019). Dynamic trajectory planning and tracking for autonomous vehicle with obstacle avoidance based on model predictive control. IEEE Access, 7, 132074-132086.

[19] Al-Mayyahi, A., Aldair, A. A., & Rashid, A. T. (2020). Intelligent control of mobile robot via waypoints using nonlinear model predictive controller and quadratic bezier curves algorithm. Journal of Electrical Engineering & Technology, 15(4), 1857-1870.

[20] Kim, E., Kim, J., & Sunwoo, M. (2014). Model predictive control strategy for smooth path tracking of autonomous vehicles with steering actuator dynamics. International Journal of Automotive Technology, 15, 1155-1164.

[21] Britzelmeier, A., & Gerdts, M. (2020). A nonsmooth newton method for linear model-predictive control in tracking tasks for a mobile robot with obstacle avoidance. IEEE Control Systems Letters, 4(4), 886-891.

[22] Yao, Z., Zhao, B., Yuan, T., Jiang, H., & Jiang, Y. (2020). Reducing gasoline consumption in mixed connected automated vehicles environment: A joint optimization framework for traffic signals and vehicle trajectory. Journal of cleaner production, 265, 121836.

[23] Yang, X., Seethaler, R., Zhan, C., Lu, D., & Zhao, W. (2019). A model predictive contouring error precompensation method. IEEE Transactions on Industrial Electronics, 67(5), 4036-4045.

[24] Hide, C., Moore, T., & Smith, M. (2004, April). Adaptive Kalman filtering algorithms for integrating GPS and low cost INS. In Plans 2004. Position location and navigation symposium (ieee cat. no. 04ch37556) (pp. 227-233). IEEE.

[25] Bai, G., Liu, L., Meng, Y., Luo, W., Gu, Q., & Wang, J. (2019). Path tracking of wheeled mobile robots based on dynamic prediction model. IEEE Access, 7, 39690-39701.

[26] Bai, G., Meng, Y., Liu, L., Luo, W., Gu, Q., & Li, K. (2019). A new path tracking method based on multilayer model predictive control. Applied sciences, 9(13), 2649.

[27] Hang, P., Huang, S., Chen, X., & Tan, K. K. (2021). Path planning of collision avoidance for unmanned ground vehicles: A nonlinear model predictive control approach. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 235(2), 222-236.

[28] Gia Luan, P., & Thinh, N. T. (2020). Real-time hybrid navigation system-based path planning and obstacle avoidance for mobile robots. Applied sciences, 10(10), 3355.

[29] C. Hong, Model Predictive control, Science Publishing House, Beijing, 2013.

[30] Yan, Z., Gong, P., Zhang, W., & Wu, W. (2020). Model predictive control of autonomous underwater vehicles for trajectory tracking with external disturbances. Ocean Engineering, 217, 107884.

[31] Howard, S., Ko, H. L., & Alexander, W. E. (1996, March). Parallel processing and stability analysis of the Kalman filter. In Conference Proceedings of the 1996 IEEE Fifteenth Annual International Phoenix Conference on Computers and Communications (pp. 366-372). IEEE.

[32] Oyelere, S. S. (2014). The application of model predictive control (MPC) to fast systems such as autonomous ground vehicles (AGV). IOSR Journal of Computer Engineering, 3(3), 27-37.

[33] Ephraim, Y., & Merhav, N. (2002). Hidden markov processes. IEEE Transactions on information theory, 48(6), 1518-1569.


Cite This Article
APA Style
Wu, H., Chen, Y., & Qin, H. (2024). Model Predictive Control for Enhanced Trajectory Tracking of Autonomous Deep-Sea Tracked Mining Vehicles. IECE Transactions on Intelligent Unmanned Systems, 1(1), 31–43. https://doi.org/10.62762/TIUS.2024.557673

Article Metrics
Citations:

Crossref

0

Scopus

0

Web of Science

0
Article Access Statistics:
Views: 1274
PDF Downloads: 116

Publisher's Note
IECE stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions
IECE or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
IECE Transactions on Intelligent Unmanned Systems

IECE Transactions on Intelligent Unmanned Systems

ISSN: 2998-9140 (Online)

Email: [email protected]

Portico

Portico

All published articles are preserved here permanently:
https://www.portico.org/publishers/iece/

Copyright © 2024 Institute of Emerging and Computer Engineers Inc.