Heading Control System Design for a Micro-USV Based on an Adaptive Expert S-PID Algorithm

Open access


The process of heading control system design for a kind of micro-unmanned surface vessel (micro-USV) is addressed in this paper and a novel adaptive expert S-PID algorithm is proposed. First, a motion control system for the micro-USV is designed based on STM32-ARM and the PC monitoring system is developed based on Labwindows/CVI. Second, by combining the expert control technology, S plane and PID control algorithms, an adaptive expert S-PID control algorithm is proposed for heading control of the micro-USV. Third, based on SL micro-USV developed in this paper, a large number of pool experiments and lake experiments are carried out, to verify the effectiveness and reliability of the motion control system designed and the heading control algorithm proposed. A great amount of comparative experiment results shows the superiority of the proposed adaptive expert S-PID algorithm in terms of heading control of the SL micro-USV.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • 1. Woo J. Kim N. Vision based target motion analysis and collision avoidance of unmanned surface vehicles. Proceedings of the Institution of Mechanical Engineers Part M-Journal of Engineering for the Maritime Environment 2016 230(4): 566-578.

  • 2. Dong Z.P. Wan L. Liu T. et al. Horizontal plane trajectory tracking control of an underactuated unmanned marine vehicle in the presence of ocean currents. International Journal of Advanced Robotic Systems 2016 13:83 1-14.

  • 3. Nuss A. Blcakburn T. Garstenauer A. Toward resilient unmanned maritime systems (UMS). Naval Engineers Journal 2016 128(2): 65-71.

  • 4. Przyborski M. Information about dynamics of the sea surface as a means to improve safety of the unmanned vessel at sea. Polish Maritime Research 2016 23(4): 3-7.

  • 5. Caccia M. Bono R. Bruzzon G. et al. Sampling sea surfaces with SESAMO. IEEE Robotics and Automation Magazine 2015 12(3): 95-105.

  • 6. Do K D Pan J. Robust path-following of underactuated ships: theory and experiments on a model ship. Ocean Engineering 2006 33(10): 1354-1372

  • 7. Sohn S I Oh J H Lee Y S et al. Design of a fuel-cell-powered catamaran-type unmanned surface vehicle. IEEE Journal of Oceanic Engineering 2015 40(2): 388-396

  • 8. Wu G X Sun H B Zou J et al. The basic motion control strategy for the water-jet-propelled USV. In the proceedings of 2009 IEEE International Conference on Mechatronics and Automation Changchun China August 9-12 2009 IEEE pp: 611-616.

  • 9. Li Z Sun J. Disturbance compensating model predictive control with application to ship heading control. IEEE Transactions on Control Systems Technology 2012 20(1): 257-265.

  • 10. Sonnenburg C R Woolsey C A. Modeling identification and control of an unmanned surface vehicle. Journal of Field Robotics 2013 30(3): 371-398.

  • 11. Kurowski M Haghani A Koschorrek P et al. Guidance navigation and control of unmanned surface vehicles. AT-Automatisierungstechnik 2015 63(5): 355-367.

  • 12. Nad D Miskovic N Mandic F. Navigation guidance and control of an overactuated marine surface vehicle. Annual Reviews in Control 2014 40: 172-181.

  • 13. Wang N. Lv S.L. Liu Z.Z. Global finite time heading control of surface vehicles. Neurocomputing 2016 175: 662-666.

  • 14. Veksler A. Johansen T. A. Borrelli F. et al. Dynamic position with model predictive control. IEEE Transactions on Control Systems Technology 2016 24(4): 1340-1353.

  • 15. Wang Y. L. Han Q. L. Network based heading control and rudder oscillation reduction for unmanned surface vehicles. IEEE Transactions on Control Systems Technology 2016: 1-12.

  • 16. Dong Z. P. Wan L. Li Y. M. et al. Trajectory tracking control of underactuated USV based on modified backstepping. International Journal of Naval Architecture and Ocean Engineering 2015 7(5): 817-832.

  • 17. Alfi A. Shokrzadeh A. Asadi M. Reliability analysis of H-infinity control for a container ship in way-point tracking. Applied Ocean Research 2015 52: 309-316.

Journal information
Impact Factor

IMPACT FACTOR 2018: 1.214
5-year IMPACT FACTOR: 1.086

CiteScore 2018: 1.48

SCImago Journal Rank (SJR) 2018: 0.391
Source Normalized Impact per Paper (SNIP) 2018: 1.141

Cited By
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 383 163 3
PDF Downloads 403 159 1