Papers
Topics
Authors
Recent
Search
2000 character limit reached

ASV Station Keeping under Wind Disturbances using Neural Network Simulation Error Minimization Model Predictive Control

Published 11 Oct 2023 in cs.RO and cs.LG | (2310.07892v1)

Abstract: Station keeping is an essential maneuver for Autonomous Surface Vehicles (ASVs), mainly when used in confined spaces, to carry out surveys that require the ASV to keep its position or in collaboration with other vehicles where the relative position has an impact over the mission. However, this maneuver can become challenging for classic feedback controllers due to the need for an accurate model of the ASV dynamics and the environmental disturbances. This work proposes a Model Predictive Controller using Neural Network Simulation Error Minimization (NNSEM-MPC) to accurately predict the dynamics of the ASV under wind disturbances. The performance of the proposed scheme under wind disturbances is tested and compared against other controllers in simulation, using the Robotics Operating System (ROS) and the multipurpose simulation environment Gazebo. A set of six tests were conducted by combining two wind speeds (3 m/s and 6 m/s) and three wind directions (0$\circ$, 90$\circ$, and 180$\circ$). The simulation results clearly show the advantage of the NNSEM-MPC over the following methods: backstepping controller, sliding mode controller, simplified dynamics MPC (SD-MPC), neural ordinary differential equation MPC (NODE-MPC), and knowledge-based NODE MPC (KNODE-MPC). The proposed NNSEM-MPC approach performs better than the rest in 4 out of the 6 test conditions, and it is the second best in the 2 remaining test cases, reducing the mean position and heading error by at least 31\% and 46\% respectively across all the test cases. In terms of execution speed, the proposed NNSEM-MPC is at least 36\% faster than the rest of the MPC controllers. The field experiments on two different ASV platforms showed that ASVs can effectively keep the station utilizing the proposed method, with a position error as low as $1.68$ m and a heading error as low as $6.14{\circ}$ within time windows of at least $150$s.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (29)
  1. Y. Qu and L. Cai, “Nonlinear station keeping control for underactuated unmanned surface vehicles to resist environmental disturbances,” Ocean Engineering, vol. 246, p. 110603, 2022.
  2. E. I. Sarda, H. Qu, I. R. Bertaska, and K. D. von Ellenrieder, “Station-keeping control of an unmanned surface vehicle exposed to current and wind disturbances,” Ocean Engineering, vol. 127, pp. 305–324, 2016.
  3. E. I. Sarda and M. R. Dhanak, “Launch and recovery of an autonomous underwater vehicle from a station-keeping unmanned surface vehicle,” IEEE Journal of Oceanic Engineering, vol. 44, no. 2, pp. 290–299, 2018.
  4. B. R. Page, R. Lambert, J. Chavez-Galaviz, and N. Mahmoudian, “Underwater docking approach and homing to enable persistent operation,” Frontiers in Robotics and AI, vol. 8, p. 621755, 2021.
  5. B. Li, B. R. Page, B. Moridian, and N. Mahmoudian, “Collaborative mission planning for long-term operation considering energy limitations,” IEEE Robotics and Automation Letters, vol. 5, no. 3, pp. 4751–4758, 2020.
  6. M. Bresciani, G. Peralta, F. Ruscio, L. Bazzarello, A. Caiti, and R. Costanzi, “Cooperative asv/auv system exploiting active acoustic localization,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2021, pp. 4337–4342.
  7. J. Busquets, F. Zilic, C. Aron, and R. Manzoliz, “Auv and asv in twinned navigation for long term multipurpose survey applications,” in 2013 MTS/IEEE OCEANS-Bergen.   IEEE, 2013, pp. 1–10.
  8. Y. Jiang, Z. Peng, C. Meng, L. Liu, D. Wang, and T. Li, “Data-driven finite control set model predictive speed control of an autonomous surface vehicle subject to fully unknown kinetics and propulsion dynamics,” Ocean Engineering, vol. 264, p. 112474, 2022.
  9. A. Pereira, J. Das, and G. S. Sukhatme, “An experimental study of station keeping on an underactuated asv,” in 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.   IEEE, 2008, pp. 3164–3171.
  10. F. Kitamura, H. Sato, K. Shimada, and T. Mikami, “Estimation of wind force acting on huge floating ocean structures,” in Oceans’ 97. MTS/IEEE Conference Proceedings, vol. 1.   IEEE, 1997, pp. 197–202.
  11. D. Schlipf, L. Y. Pao, and P. W. Cheng, “Comparison of feedforward and model predictive control of wind turbines using lidar,” in 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).   IEEE, 2012, pp. 3050–3055.
  12. K. Seel, E. I. Grøtli, S. Moe, J. T. Gravdahl, and K. Y. Pettersen, “Neural network-based model predictive control with input-to-state stability,” in 2021 American Control Conference (ACC).   IEEE, 2021, pp. 3556–3563.
  13. P.-F. Xu, C.-B. Han, H.-X. Cheng, C. Cheng, and T. Ge, “A physics-informed neural network for the prediction of unmanned surface vehicle dynamics,” Journal of Marine Science and Engineering, vol. 10, no. 2, p. 148, 2022.
  14. K. Y. Chee, T. Z. Jiahao, and M. A. Hsieh, “Knode-mpc: A knowledge-based data-driven predictive control framework for aerial robots,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 2819–2826, 2022.
  15. M. Forgione and D. Piga, “Continuous-time system identification with neural networks: Model structures and fitting criteria,” European Journal of Control, vol. 59, pp. 69–81, 2021.
  16. R. T. Q. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud, “Neural ordinary differential equations,” in Advances in Neural Information Processing Systems, S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, Eds., vol. 31.   Curran Associates, Inc., 2018.
  17. E. Kaiser, J. N. Kutz, and S. L. Brunton, “Sparse identification of nonlinear dynamics for model predictive control in the low-data limit,” Proceedings of the Royal Society A, vol. 474, no. 2219, p. 20180335, 2018.
  18. T. Z. Jiahao, K. Y. Chee, and M. A. Hsieh, “Online dynamics learning for predictive control with an application to aerial robots,” in Conference on Robot Learning.   PMLR, 2023, pp. 2251–2261.
  19. J. Saint-Donat, N. Bhat, and T. J. McAvoy, “Neural net based model predictive control,” International Journal of Control, vol. 54, no. 6, pp. 1453–1468, 1991.
  20. K. Chua, R. Calandra, R. McAllister, and S. Levine, “Deep reinforcement learning in a handful of trials using probabilistic dynamics models,” Advances in neural information processing systems, vol. 31, 2018.
  21. T. Salzmann, E. Kaufmann, M. Pavone, D. Scaramuzza, and M. Ryll, “Neural-mpc: Deep learning model predictive control for quadrotors and agile robotic platforms,” arXiv preprint arXiv:2203.07747, 2022.
  22. G. Williams, N. Wagener, B. Goldfain, P. Drews, J. M. Rehg, B. Boots, and E. A. Theodorou, “Information theoretic mpc for model-based reinforcement learning,” in 2017 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2017, pp. 1714–1721.
  23. M. A. Schoener, “Global estimation methodology for wave adaptation modular vessel dynamics using a genetic algorithm,” 2019.
  24. B. Amos, I. Jimenez, J. Sacks, B. Boots, and J. Z. Kolter, “Differentiable mpc for end-to-end planning and control,” Advances in neural information processing systems, vol. 31, 2018.
  25. B. Page, R. Lambert, J. Chavez-Galaviz, and N. Mahmoudian, “Path following using rendezvous dubins curves and integral line-of-sight for unmanned marine systems,” Field Robotics, vol. 2, pp. 1920–1942, 2022.
  26. S. Vaidyanathan and A. T. Azar, “Chapter 1 - an introduction to backstepping control,” in Backstepping Control of Nonlinear Dynamical Systems, ser. Advances in Nonlinear Dynamics and Chaos (ANDC), S. Vaidyanathan and A. T. Azar, Eds.   Academic Press, 2021, pp. 1–32.
  27. K. Young, V. Utkin, and U. Ozguner, “A control engineer’s guide to sliding mode control,” IEEE Transactions on Control Systems Technology, vol. 7, no. 3, pp. 328–342, 1999.
  28. F. Muñoz, I. González-Hernández, S. Salazar, E. S. Espinoza, and R. Lozano, “Second order sliding mode controllers for altitude control of a quadrotor uas: Real-time implementation in outdoor environments,” Neurocomputing, vol. 233, pp. 61–71, 2017, sI: CCE 2015.
  29. A. M. Yazdani, K. Sammut, O. Yakimenko, and A. Lammas, “A survey of underwater docking guidance systems,” Robotics and Autonomous systems, vol. 124, p. 103382, 2020.
Citations (3)
View on Semantic Scholar

Summary

No one has generated a summary of this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Sign Up to Summarize

Paper to Video (Beta)

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Sign Up to Generate

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now

Continue Learning

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up