A Practical Specialization of MDA/MBSE Approach to Develop AUV Controllers

Abstract

The model-driven architecture (MDA)/model-based systems engineering (MBSE) approach, in combination with the real-time Unified Modeling Language (UML)/Systems Modeling Language (SysML), unscented Kalman filter (UKF) algorithm, and hybrid automata, are specialized to conveniently analyze, design, and implement controllers of autonomous underwater vehicles (AUVs). The dynamics and control structure of AUVs are adapted and integrated with the specialized features of the MDA/MBSE approach as follows. The computation-independent model is defined by the specification of a use case model together with the UKF algorithm and hybrid automata and is used in intensive requirement analysis. The platform-independent model (PIM) is then built by specializing the real-time UML/SysML’s features, such as the main control capsules and their dynamic evolutions, which reflect the structures and behaviors of controllers. The detailed PIM is subsequently converted into the platform-specific model by using open-source platforms to quickly implement and deploy AUV controllers. The study ends with a trial trip and deployment results for a planar trajectory-tracking controller of a miniature AUV with a torpedo shape.

This is a preview of subscription content, log in to check access.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13

References

  1. Allotta B, Caitib A, Costanzi R, Fanelli F, Fenucci D, Meli E, Ridolfi A (2016a) A new AUV navigation system exploiting unscented Kalman filter. Ocean Eng, Elsevier, ISSN 0029-8018 113:121–132. https://doi.org/10.1016/j.oceaneng.2015.12.058

    Article  Google Scholar 

  2. Allotta B, Conti R, Costanzi R, Fanelli F, Gelli J, Meli E, Monni N, Ridolfi A, Rindi A (2016b) A low cost autonomous underwater vehicle for patrolling and monitoring. J Eng Marit Environ, SAGE Publishing, ISSN 1475-0902 231:740–749. https://doi.org/10.1177/1475090216681354

    Article  Google Scholar 

  3. Antonelli G (2006) Underwater robots - motion and force control of vehicle-manipulator systems. Springer, Heidelberg

    Google Scholar 

  4. Arduino (2018) Open-source electronics prototyping platform for hardware and software. Arduino. Available from http://www.arduino.cc/. Accessed on January 2018

  5. Bar-Shalom Y, Li XR, Kirubarajan T (2001) Estimation with applications to tracking and navigation- theory algorithms and software. John Wiley & Sons, USA

    Google Scholar 

  6. Bhopale P, Kazi F, Singh N (2019) Reinforcement learning based obstacle avoidance for autonomous underwater vehicle. J Mar Sci Appl, Springer, ISSN 1671-9433 18:228–238. https://doi.org/10.1007/s11804-019-00089-3

    Article  Google Scholar 

  7. Brignone L, Raugel E, Opderbecke J, Rigaud V, Piasco R, Ragot S (2015) First sea trials of HROV the new hybrid vehicle developed by IFREMER. In: OCEANS 2015 - Genova, Genova, Italy, IEEE, pp 1–7. https://doi.org/10.1109/OCEANS-Genova.2015.7271682

  8. Carloni LP, Passerone R, Pinto A, Sangiovanni VA (2006) Languages and tools for hybrid systems design. Now Publishers Inc, Boston

    Google Scholar 

  9. Cui W (2019) An overview of submersible research and development in China. J Mar Sci Appl, Springer, ISSN 1671-9433 17:459–470. https://doi.org/10.1007/s11804-019-00121-6

    Article  Google Scholar 

  10. Diem PG, Hien NV, Khanh NP (2013) An object-oriented analysis and design model to implement controllers for quadrotor UAVs by specializing MDA’s features with hybrid automata and real-time UML. WSEAS Trans Syst, E-ISSN 2224-2678 12:483–496

    Google Scholar 

  11. Douglass BP (2011) Design patterns for embedded Systems in C - an embedded software engineering toolkit, 1st edn. Elsevier, Oxford

    Google Scholar 

  12. Douglass BP (2014) Real-time UML workshop for embedded systems, 2nd edn. Elsevier, Oxford

    Google Scholar 

  13. Eslami M, Chin CS, Nobakhti AJ (2018) Robust modeling, sliding-mode controller, and simulation of an underactuated ROV under parametric uncertainties and disturbances. J Mar Sci Appl, Springer, ISSN 1671-9433. https://doi.org/10.1007/s11804-018-0037-1:1-15

  14. Fishwick PA (ed) (2007) Handbook of dynamic system modeling. Taylor & Francis Group, USA

    Google Scholar 

  15. Fossen TI (2002) Marine control systems: guidance, navigation and control of ships, rigs and underwater vehicles. Marine Cybernetics, Trondheim ISBN 82-92356-00-2

    Google Scholar 

  16. Fossen TI (2011) Handbook of marine craft hydrodynamics and motion control. John Wiley & Sons, United Kingdom

    Google Scholar 

  17. Fritzson P (2015) Principles of object-oriented modeling and simulation with modelica 3.3: a cyber-physical approach, 2nd edn. Wiley-IEEE Press, USA

    Google Scholar 

  18. Gamma E, Helm R, Johnson R, Vlissides J (1995) Design patterns: elements of reusable object-oriented software. Addison-Wesley, Oxford

    Google Scholar 

  19. Henzinger TA, Kopke PW, Puri A, Varaiya P (1998) What's decidable about hybrid automata? J Comput Syst Sci, Elsevier, ISSN 0022-0000 57:94–124. https://doi.org/10.1006/jcss.1998.1581

    MathSciNet  Article  MATH  Google Scholar 

  20. Hien NV, Soriano T (2012) A model transformation process to realize controllers of ship autopilot systems by the specialized MDA’s features with UML/SysML. In: Proceedings of IEEE Conference on MECATRONICS-REM 2012, ISBN 978-1-4673-4771-6, Paris, France. IEEE, pp 20–26

  21. Hien NV, Anh TV, Tuan KM et al. (2013) Research, design and manufacture control systems with the integration of object-oriented technology (MDA & Real-Time UML) and navigation units (INS/GPS) for autonomous underwater vehicles, final report of research project, funded by the state, code: KC03.TN05/11-15. Hanoi University of Science and Technology, Hanoi, Vietnam

  22. Hien NV, He NV, Diem PG (2018) A model-driven implementation to realize controllers for autonomous underwater vehicles. Appl Ocean Res, Elsevier, ISSN 0141-1187 78:307–319. https://doi.org/10.1016/j.apor.2018.06.020

    Article  Google Scholar 

  23. IBM (2018) IBM Rational’s methodology, software, Online Documentation and Training Kits. IBM. Available from https://my15.digitalexperience.ibm.com/b73a5759-c6a6-4033-ab6b-d9d4f9a6d65b/dxsites/151914d1-03d2-48fe-97d9-d21166848e65/academic/home. Accessed on July 2018

  24. INCOSE (2007) Systems Engineering Vision 2020, Version 2.03. INCOSE, San Diego, CA 92111-2222, USA

  25. INCOSE (2014) Systems Engineering Vision 2025. INCOSE, San Diego, CA 92111-2222, USA

  26. InvenSense (2018) Sensor System on Chip. Available from http://www.invensense.com/. Accessed on January 2018

  27. Karkoub M, Wu HM, Hwang CL (2017) Nonlinear trajectory-tracking control of an autonomous underwater vehicle. Ocean Eng, Elsevier, ISSN 0029-8018 145:188–198. https://doi.org/10.1016/j.oceaneng.2017.08.025

    Article  Google Scholar 

  28. Lantos B, Márton L (2011) Nonlinear control of vehicles and robots. Springer, London

    Google Scholar 

  29. Lekkas AM, Fossen TI (2014) Integral LOS path following for curved paths based on a monotone cubic hermite spline parametrization. IEEE Trans Control Syst Technol, ISSN 1063-6536 22:2287–2301. https://doi.org/10.1109/TCST.2014.2306774

    Article  Google Scholar 

  30. Li W, Wu W, Wang J, Wu M (2014) A novel backtracking navigation scheme for autonomous underwater vehicles. Measurement, Elsevier, ISSN 0263-2241 47:496–504. https://doi.org/10.1016/j.measurement.2013.09.022

    Article  Google Scholar 

  31. MahmoudZadeh S, Powers DMW, Yazdani AM, Sammut K, Atyabi A (2018) Efficient AUV path planning in time-variant underwater environment using differential evolution algorithm. J Mar Sci Appl, Springer, ISSN 1671-9433 17:585–591. https://doi.org/10.1007/s11804-018-0034-4

    Article  Google Scholar 

  32. MathWorks (2018) MATLAB and Simulink products. MathWorks. Available from https://www.mathworks.com/. Accessed on July 2018

  33. OMG (2011) UML Profile for MARTE: Modeling and Analysis of Real-Time Embedded Systems Version 1.1

  34. OMG (2014) Model Driven Architecture (MDA): Guide revision 2.0 of MDA Guide Version 1.0.1 (12th June 2003). OMG Document ormsc/2014-06-01

  35. OMG (2015) Documents Associated With Unified Modeling Language™ (UML® Version 2.5). OMG

  36. OMG (2017) SysML Specifications Version 1.5. OMG

  37. OpenModelica (2018) OpenModelica. OpenModelica software, version 1.12. OpenModelica. Available from https://www.openmodelica.org/. Accessed on April 2018

  38. Ribas D, Ridao P, Melchiorri C, Palli G, Fernández JJ, Sanz PJ (2015) I-AUV mechatronics integration for the TRIDENT FP7 project. IEEE/ASME Trans Mechatron, ISSN 1083-4435 20:2583–2592. https://doi.org/10.1109/TMECH.2015.2395413

    Article  Google Scholar 

  39. Sakairi T, Palachi E, Cohen C, Hatsutori Y, Shimizu J, Miyashita H (2013) Model based control system design using SysML, simulink, and computer algebra system. J Control Sci Eng, Hindawi, ISSN 1687-5249 2013:14. https://doi.org/10.1155/2013/485380

    MathSciNet  Article  MATH  Google Scholar 

  40. Selic B, Gerard S (2014) Modeling and analysis of real-time and embedded systems with UML and MARTE. Elsevier, USA

    Google Scholar 

  41. Shariati H, Moosavi H, Danesh M (2019) Application of particle filter combined with extended Kalman filter in model identification of an autonomous underwater vehicle based on experimental data. Ocean Eng, Elsevier, ISSN 0029-8018 82:32–40. https://doi.org/10.1016/j.apor.2018.10.015

    Article  Google Scholar 

  42. Shojaei K, Dolatshahi M (2017) Line-of-sight target tracking control of underactuated autonomous underwater vehicles. Ocean Eng, Elsevier, ISSN 0029-8018 133:244–252. https://doi.org/10.1016/j.oceaneng.2017.02.007

    Article  Google Scholar 

  43. SNAME (1950) Nomenclature for treating the motion of a submerged body through a fluid, technical and research bulletin No. 1-5. SNAME (the Society of Naval Architects and Marine Engineers), New York 18, N. Y., USA

  44. Soriano T, Hien NV, Tuan KM, Anh TV (2016) An object-unified approach to develop controllers for autonomous underwater vehicles. Mechatron Sci Intelligent Mach, Elsevier, ISSN 0957-4158 35:54–70. https://doi.org/10.1016/j.mechatronics.2015.12.011

    Article  Google Scholar 

  45. u-blox (2018) Gobal leader in wireless communications and positioning semiconductors and modules for the industrial, automotive and consumer markets. u-blox. Available from https://www.u-blox.com. Accessed on July 2018

  46. Wan EA, Merwe RVD (2001) The unscented Kalman filter. In: Haykin S (ed) Kalman filtering and neural networks. Wiley, New York, pp 221–280

    Google Scholar 

  47. Wynn RB et al (2014) Autonomous underwater vehicles (AUVs): their past, present and future contributions to the advancement of marine geoscience. Mar Geol Int J Mar Geol Geochem Geophys, Elsevier, ISSN 0025-3227 352:451–468. https://doi.org/10.1016/j.margeo.2014.03.012

    Article  Google Scholar 

  48. Zheng Z, Zou Y (2016) Adaptive integral LOS path following for an unmanned airship with uncertainties based on robust RBFNN backstepping. ISA Trans, Elsevier, ISSN 0019-0578 65:210–219. https://doi.org/10.1016/j.isatra.2016.09.008

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Ngo Van Hien.

Additional information

Article Highlights

• A specialization of MDA/MBSE approach combined with the UKF algorithm and hybrid automata is performed to systematically analyze, design and implement an AUV controller.

• The designed capsule collaboration of real-time UML/SysML can be customized and reused for new control applications of various AUV types.

• A planar trajectory-tracking controller of a miniature torpedo-shaped AUV was deployed and tested.

Appendix

Appendix

An example of the main “HA_Q_AUV.h” header and “HA_Q_AUV.h.cpp” implementation files of HA library for the developed AUV controller were implemented, verified, and compiled to fit in ATMEGA32-U2 and STM32 Cortex-M4 microcontrollers by using Arduino IDE version 1.8.0.

figurebfigureb

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Van Hien, N., Diem, P.G. A Practical Specialization of MDA/MBSE Approach to Develop AUV Controllers. J. Marine. Sci. Appl. (2020). https://doi.org/10.1007/s11804-020-00151-5

Download citation

Keywords

  • Autonomous underwater vehicles (AUVs)
  • AUV control
  • Model-based mechatronic system design
  • Unscented Kalman filter (UKF)
  • Hybrid automata
  • Real-time UML/SysML
  • MDA/MBSE