Allocation Integrated Sliding Mode Control for Heading Autonomous Underwater Vehicles with Varying Speeds

Khac Tiep Do1,   , Van Tien Nguyen1,
1 Faculty of Electrical and Electronic Engineering, Vietnam Maritime University, Haiphong

Main Article Content

Abstract

This paper addresses the challenge of robust heading control for Autonomous Underwater Vehicles (AUVs) operating under variable speed conditions, subject to system nonlinearities, parametric uncertainties, and actuator constraints. We propose a hierarchical control architecture that integrates Sliding Mode Control (SMC) with an optimization-based Control Allocation (CA) scheme. The outer-loop SMC guarantees robustness by computing the required total yaw moment to reject disturbances and track the reference heading. The inner-loop CA then optimally distributes this virtual control effort to individual actuators, explicitly accounting for saturation limits and speed-dependent actuator effectiveness. The performance of the proposed SMC-CA strategy is validated through comprehensive numerical simulations. Results indicate that the system maintains high tracking accuracy and stability across a wide speed range, demonstrating significantly faster transient response and superior disturbance rejection compared to a conventional PID controller in various scenarios, including step changes and abrupt speed variations.

Article Details

References

[1] R. B.Wynn et al., “Autonomous underwater vehicles: Their past, present and future contributions to the advancement of marine geoscience,” Marine Geology, vol. 352, pp. 451-468, 2014.
[2] P. Ridao et al., “Intervention AUVs: The next challenge,” IFAC-PapersOnLine, vol. 48, no. 2, pp. 10-17, 2015.
[3] T. I. Fossen, Handbook of marine craft hydrodynamics and motion control, John Wiley & Sons, 2011.
[4] G. Antonelli, Underwater robots, Springer, 2014.
[5] Y. -H. Lin and Y. -C. Chiu, “The estimation of hydrodynamic coefficients of an autonomous underwater vehicle by comparing a dynamic mesh model with a horizontal planar motion mechanism experiment,” Ocean Engineering, vol. 249, pp. 110847, 2022.
[6] M. D. L. Casado and F. J. Velasco, “Design and simulation of X-rudder AUV's motion control,” Ocean Engineering, vol. 137, pp. 204-214, 2017.
[7] D. Li and L. Du, “AUV Trajectory Tracking Models and Control Strategies: A Review,” Journal of Marine Science and Engineering, vol. 9, no. 9, pp. 1020, 2021.
[8] C. Edwards and S. K. Spurgeon, Sliding mode control: theory and applications, CRC Press, 1998.
[9] O. Kamal, “Robust heading stabilization and control for a class of autonomous underwater vehicles using nonlinear state estimators,” 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST), 2019.
[10] T. A. Johansen and T. I. Fossen, “Control allocation—A survey,” Automatica, vol. 49, no. 5, pp. 1087-1103, 2013.
[11] T. A. Johansen, T. I. Fossen, and S. Berge, “Constrained nonlinear control allocation with singularity avoidance using sequential quadratic programming,” IEEE Transactions on Control Systems Technology, vol. 12, no. 1, pp. 211-216, 2004.
[12] T. M. Blaha, E. J. J. Smeur, and B. D. W. Remes, “A survey of optimal control allocation for aerial vehicle control,” Actuators , vol. 12, no. 7, pp. 282, 2023.
[13] C. Yuan, C. Shuai, and J. Ma, “An efficient control allocation algorithm for over-actuated AUVs trajectory tracking with fault-tolerant control,” Ocean Engineering, vol. 273, pp. 113976, 2023.
[14] W. Remmas and A. Chemori, “Fault-tolerant control allocation for a bio-inspired underactuated AUV in the presence of actuator failures: Design and experiments,” Ocean Engineering, vol. 286, pp. 115595, 2023.
[15] W. Wang et al., “Robust trajectory tracking and control allocation of X-rudder AUV with actuator uncertainty,” Control Engineering Practice, vol. 136, pp. 105535, 2023.