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Derivative free optimal thrust allocation in ship dynamic positioning based on direct search algorithms

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Języki publikacji
EN
Abstrakty
EN
In dynamic positioning systems, nonlinear cost functions, as well as nonlinear equality and inequality constraints within optimal thrust allocation procedures cannot be handled directly by means of the solvers like industry-standardized quadratic programing (QP), at least not without appropriate linearization technique applied, which can be computationally very expensive. Thus, if optimization requirements are strict, and problem should be solved for nonlinear objective function with nonlinear equality and inequality constraints, than one should use some appropriate nonlinear optimization technique. The current state-of-the-art in nonlinear optimization for gradient-based algorithms is surely the sequential quadratic programing (SQP), both for general applications and specific thrust allocation problems. On the other hand, in recent time, one can also notice the increased applications of gradient-free optimization methods in various engineering problems. In this context, the implementation of selected derivative free direct search algorithms in optimal thrust allocation is proposed and discussed in this paper, and avenues for future research are provided.
Twórcy
autor
  • University of Rijeka, Rijeka, Croatia
  • University of Rijeka, Rijeka, Croatia
  • Centre for Autonomous Marine Operations and Systems (AMOS), NTNU, Trondheim, Norway
Bibliografia
  • 1. Audet, C., Dennis, J.E., 2009. A Progressive Barrier for Derivative-Free Nonlinear Programming. SIAM J. Optim. 20, 445–472.
  • 2. Audet, C., Dennis, J.E., 2006. Mesh Adaptive Direct Search Algorithms for Constrained Optimization. SIAM J. Optim. 17, 188–217.
  • 3. Audet, C., Dennis, J.E., 2004. A Pattern Search Filter Method for Nonlinear Programming without Derivatives. SIAM J. Optim. 14, 980–1010.
  • 4. Audet, C., Hare, W., 2017. Derivative-Free and Blackbox Optimization, Springer Series in Operations Research and Financial Engineering. Springer International Publishing, Cham.
  • 5. Audet, C., Dennis, J.E., Le Digabel, S., 2010. Globalization strategies for Mesh Adaptive Direct Search. Comput. Optim. Appl. 46, 193–215.
  • 6. Conn, A.R., Gould, N.I.M., Toint, P.L., 1997. A Globally Convergent Augmented Lagrangian Barrier Algorithm for Optimization with General Inequality Constraints and Simple Bounds. Math. Comput. 66, 261–288.
  • 7. Conn, A.R., Gould, N.I.M., Toint, P., 1991. A Globally Convergent Augmented Lagrangian Algorithm for Optimization with General Constraints and Simple Bounds. SIAM J. Numer. Anal. 28, 545–572.
  • 8. Dennis Jr., J.E., Price, C.J., Coope, I.D., 2004. Direct Search Methods for Nonlinearly Constrained Optimization Using Filters and Frames. Optim. Eng. 5, 123–144
  • 9. Gierusz, W., Tomera, M., 2006. Logic thrust allocation applied to multivariable control of the training ship. Control Eng. Pract. 14, 511–524.
  • 10. Jenssen, N.A., Realfsen, B., 2006. Power Optimal Thruster Allocation, in: Proceedings of the Dynamic Positioning Conference, Houston, TX, USA.
  • 11. Johansen, T.A., Fossen, T.I., Berge, S.P., 2004. Constrained Nonlinear Control Allocation With Singularity Avoidance Using Sequential Quadratic Programming. IEEE Trans. Control Syst. Technol. 12, 211–216.
  • 12. Kolda, T.G., Lewis, R.M., Torczon, V., 2006. A generating set direct search augmented Lagrangian algorithm for optimization with a combination of general and linear constraints. SANDIA REPORT SAND2006-5315, Sandia National Laboratories, Albuquerque, New Mexico, California, USA.
  • 13. Kolda, T.G., Lewis, R.M., Torczon, V., 2003. Optimization by Direct Search: New Perspectives on Some Classical and Modern Methods. SIAM Rev. 45, 385–482.
  • 14. Le Digabel, S., 2011. Algorithm 909. ACM Trans. Math. Softw. 37, 1–15.
  • 15. Leavitt, J.A., 2008. Optimal Thrust Allocation in a Dynamic Positioning System. SNAME Trans. 116, 153–165.
  • 16. Liang, C.C., Cheng, W.H., 2004. The optimum control of thruster system for dynamically positioned vessels. Ocean Eng. 31, 97–110.
  • 17. Snijders, J.G., 2005. Wave Filtering and Thruster Allocation for Dynamic Positioned Ships. M.Sc. Thesis. Delft University of Technology, Delft, the Netherlands.
  • 18. Sørdalen, O.J., 1997. Optimal thrust allocation for marine vessels. Control Eng. Pract. 5, 1223–1231.
  • 19. Torczon, V., 1997. On the Convergence of Pattern Search Algorithms. SIAM J. Optim. 7, 1–25.
  • 20. Tröltzsch, A., 2016. A sequential quadratic programming algorithm for equality-constrained optimization without derivatives. Optim. Lett. 10, 383–399.
  • 21. Wit, C. de, 2009. Optimal Thrust Allocation Methods for Dynamic Positioning of Ships. M.Sc. Thesis. Delft University of Technology, Delft, the Netherlands.
  • 22. Yang, S.-Z., Wang, L., S., Z., 2011a. Optimal Thrust Allocation Based on Fuel-efficiency for Dynamic Positioning System. J. Sh. Mech. 15, 217–226.
  • 23. Yang, S.-Z., Wang, L., Sun, P., 2011b. Optimal thrust allocation logic design of dynamic positioning with pseudo-inverse method. J. Shanghai Jiaotong Univ. 16, 118–123.
  • 24. Zhao, D.-W., Ding, F.-G., Tan, J.-F., Liu, Y.-Q., Bian, X.-Q., 2010. Optimal Thrust Allocation based GA for Dynamic Positioning Ship, in: Proceedings of the 2010 IEEE International Conference on Mechatronics and Automation, Xi'an, China. pp. 1254–1258.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-40901182-cee0-4d5e-a79a-35e927d713ad
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