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A Model Predictive Control Approach to AUVs Motion Coordination

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Coordination Control of Distributed Systems

Part of the book series: Lecture Notes in Control and Information Sciences ((LNCIS,volume 456))

Abstract

The problem of coordinating the motion of autonomous underwater vehicles under constrained acoustic communications is formulated and investigated in the context of the model predictive control (MPC) framework. The impact of acoustic communications and perturbations on the motion performance and robustness is discussed. A reach set formulation of the MPC scheme is outlined.

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Notes

  1. 1.

    ADCP and IMU stand by acoustic Doppler current profiler, and inertial measurement unit, respectively. While the former provides water current velocity measurements, the former measures position, velocity, and orientation.

  2. 2.

    From now on, “\(T\)” in upper script will denote transposed.

  3. 3.

    The matrices \( {\Phi }^j(T) \) and \( {\Psi }^j(T) \) are obtained by integrating the piecewise constant linear system in \((x,u)\) approximating the original system over the sampling period \(T\).

  4. 4.

    State constraints are omitted to facilitate the exposition.

References

  1. Curtin T, Bellingham J, Catipovic J, Webb D (1993) Autonomous Ocean Sampling Networks. Oceanography 6(3):86–94

    Article  Google Scholar 

  2. P. Deshpande, P. Menon, and C. Edwards. Delayed static output feedback control of a network of double integrator agents. Automatica, 49(11):3498-Â3501, 2013.

    Google Scholar 

  3. Fax J, Murray M (2004) Information flow and cooperative control of vehicle formations. IEEE Trans. Automat. Control 49:1465–1476

    Article  MathSciNet  Google Scholar 

  4. Fiorelli E, Leonard N, Bhatta P, Paley D, Bachmayer R, Fratantoni D (2006) Multi-AUV Control and Adaptive Sampling in Monterey Bay. IEEE J. of Oceanic Eng. 31(4):935–948

    Article  Google Scholar 

  5. T. Fossen. Guidance and Control of Ocean Vehicles. John Wiley & Sons Ltd., 1994.

    Google Scholar 

  6. Franco E, Magni L, Parisini T, Polycarpou M, Raimondo D (2008) Cooperative constrained control of distributed agents with nonlinear dynamics and delayed information exchange: A stabilizing receding-horizon approach. IEEE Trans. Automatic Control 53:324–338

    Article  MathSciNet  Google Scholar 

  7. L. Grüne, F. Allgöwer, R. Findeisen, J. Fischer, D. Groß, U. Hanebeck, B. Kern, M. Müller, J. Pannek, M. Reble, O. Stursberg, and P. Varuttiand K. Worthmann. chapter Distributed and Networked Model Predictive Control, pages 111–167. Springer-Verlag, 2014.

    Google Scholar 

  8. L. Gruene, J. Pannek, and K. Worthmann. A networked constrained nonlinear mpc scheme. In Procs European Control Conference, Budapest, Hungary, 2009.

    Google Scholar 

  9. Jones M, Miller L, Woodruff D, Ewert D (2007) Mapping of Submerged Aquatic Vegetation Using Autonomous Underwater Vehicles in Nearshore Regions. Proc. Oceans 2007:1–7

    Google Scholar 

  10. Keviczky T, Borrelli F, Fregene K, Godbole D, Balas G (2008) Decentralized receding horizon control and coordination of autonomous vehicle formations. IEEE Trans. Control Systems Technology 16:19–33

    Article  Google Scholar 

  11. G. Kladis, P. Menon, and C. C. Edwards. Cooperative tracking for a swarm of unmanned aerial vehicles: A distributed tagaki-sugeno fuzzy framework design. In 50th IEEE Conference on Decision and Control and European Control Conference, Orlando, FL, USA December 12–15, 2011.

    Google Scholar 

  12. A. Kurzhanski and P. Varaiya. Dynamic optimization for reachability problems. J. Optimization Theory and Applications, 108, 2001.

    Google Scholar 

  13. A. Kurzhanski and P. Varaiya. Analysis and Design of Nonlinear Control Systems, chapter The Hamilton-Jacobi Type Equations for Nonlinear Target Control and Their Approximation, pages 77–90. Springer-Verlag, 2008.

    Google Scholar 

  14. Leonard N, Paley D, Lekien F, Sepulchre R, Fratantoni D, Davis R (2007) Collective Motion, Sensor Networks, and Ocean Sampling. Procs of the IEEE 95(1):48–74

    Article  Google Scholar 

  15. F. Lobo Pereira, J. Sousa, R. Gomes, and P. Calado. Reach set formulation of a model predictive control scheme. In MTNS 2012, 20th Int. Symp. on Mathematical Theory of Network of Systems, Melbourne, Australia, July 9–13, 2012.

    Google Scholar 

  16. Menon P, Edwards C (2009) Decentralised static output feedback stabilisation and synchronisation of networks. Automatica 45(12):2910–2916

    Article  MathSciNet  MATH  Google Scholar 

  17. I. Michel. The flexible, extensible and efficient toolbox of level set methods. J. of Scientific Computing, 35.

    Google Scholar 

  18. Michel I, Bayen A, Tomlin C (2005) Computing reachable sets for continuous dynamics games using level sets methods. IEEE Trans. on Automatic Control 50:980–1001

    Article  Google Scholar 

  19. P. Ogren, M. Egerstedt, and X. Hu. A control lyapunov function approach to multi-agent coordination. In 40th IEEE Conference on Decision and Control, Orlando, Florida USA, December, volume I, pages 1150–1155, 2001.

    Google Scholar 

  20. D. Popa, A. Sanderson, R. Komerska, S. Mupparapu, D. Blidberg, and S. Chappel. Adaptive sampling algorithms for multiple autonomous underwater vehicles. In IEEE/OES Autonomous Underwater Vehicles, pages 108–118, 2004.

    Google Scholar 

  21. T. Prestero. Verification of a six-degree of freedom simulation model for the remus autonomous underwater vehicle. Master’s thesis, MIT, WHOI, Cambridge, MA, 2001.

    Google Scholar 

  22. Ren W, Beard R (2008) chapter Distributed Consensus in Multi-vehicle Cooperative Control. Springer-Verlag, London

    Book  Google Scholar 

  23. Ren W, Cao Y (2011) chapter Distributed Coordination of Multi-agent Networks. Springer-Verlag, London

    Book  Google Scholar 

  24. Rigby P, Williams S, Pizarro O, Colquhoun J (2007) Effective Benthic Surveying with Autonomous Underwater Vehicles. Oceans 2007:1–6

    Google Scholar 

  25. H Riksfjord, O. Haug, and J. Hovem. Underwater acoustic networks - survey on communication challenges with transmission simulations. In Procs of SENSORCOMM ’09, pages 300–305, 2009.

    Google Scholar 

  26. H. Schmidt, J. Bellingham, M. Johnson, D. Herold, D. Farmer, and R. Pawlowicz. Real-time frontal mapping with AUVs in a coastal environment. In Proc. MTS/IEEE OCEANS ’96, pages 1094–1098, 1996.

    Google Scholar 

  27. J. Sousa, B. Maciel, and F. Pereira. Sensor systems and networked vehicles. Networks and Heterogeneous Media, 4, 2009.

    Google Scholar 

  28. J. Sousa and R. Martins. Control architecture and software tool set for networked operations of ocean vehicles. In Proc. 9th IFAC MCMC, 2012.

    Google Scholar 

  29. Stilwell D, Bishop B (2000) Platoons of underwater vehicles. IEEE Control Systems Magazine 20(6):45–52

    Google Scholar 

  30. R. Vinter. Optimal Control. Birkhauser, 2000.

    Google Scholar 

  31. Willcox J, Bellingham J, Zhang Y, Baggeroer A (2001) Performance Metrics for Oceanographic Surveys with Autonomous Underwater Vehicles. IEEE J. of Oceanic Eng. 26(4):711–725

    Article  Google Scholar 

  32. Y. Zhang and H. Mehrjerdi. A survey on multiple unmanned vehicles formation control and coordination: normal and fault situations. In ICUAS - International Conference on Unmanned Aircraft Systems, Atlanta, GA, USA, May 28–31, pages 1087–1096, 2013.

    Google Scholar 

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Correspondence to Fernando Lobo Pereira .

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Lobo Pereira, F., Borges de Sousa, J., Gomes, R., Calado, P. (2015). A Model Predictive Control Approach to AUVs Motion Coordination. In: van Schuppen, J., Villa, T. (eds) Coordination Control of Distributed Systems. Lecture Notes in Control and Information Sciences, vol 456. Springer, Cham. https://doi.org/10.1007/978-3-319-10407-2_2

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  • DOI: https://doi.org/10.1007/978-3-319-10407-2_2

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