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

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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.

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.

Polish Maritime Research

The Journal of Gdansk University of Technology

Journal Information

IMPACT FACTOR 2017: 0.763
5-year IMPACT FACTOR: 0.816

CiteScore 2017: 0.99

SCImago Journal Rank (SJR) 2017: 0.280
Source Normalized Impact per Paper (SNIP) 2017: 0.788


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