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Optimization Techniques for Solving Elliptic Control Problems with Control and State Constraints: Part 1. Boundary Control

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Abstract

We study optimal control problems for semilinear elliptic equations subject to control and state inequality constraints. In a first part we consider boundary control problems with either Dirichlet or Neumann conditions. By introducing suitable discretization schemes, the control problem is transcribed into a nonlinear programming problem. It is shown that a recently developed interior point method is able to solve these problems even for high discretizations. Several numerical examples with Dirichlet and Neumann boundary conditions are provided that illustrate the performance of the algorithm for different types of controls including bang-bang and singular controls. The necessary conditions of optimality are checked numerically in the presence of active control and state constraints.

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References

  1. A. Barclay, P.E. Gill, and J.B. Rosen, “SQP methods and their applications to numerical optimal control,” in Variational Calculus, Optimal Control and Applications, W.H. Schmidt, K. Heier, L. Bittner, and R. Bulirsch (Eds.), vol. 124, Int. Series Numer. Mathematics, Basel, Birkhäuser, 1998, pp. 207–222.

    Google Scholar 

  2. M. Bergounioux, M. Haddou, M. Hintermüller, and K. Kunisch, “A comparison of interior point methods and a Moreau-Yosida based active set strategy for constrained optimal control problems,” Preprint, Department of Mathematics, University of Orléans, France, 1998.

    Google Scholar 

  3. M. Bergounioux and K. Kunisch, “Augmented Lagrangian techniques for elliptic state constrained optimal control problems,” SIAM J. Control Optim., vol. 35, pp. 1524–1543, 1997.

    Google Scholar 

  4. J.T. Betts, “Issues in the direct transcription of optimal control problems to sparse nonlinear programs,” in Control Applications of Optimization, R. Bulirsch and D. Kraft (Eds.), vol. 115, Int. Series Numer. Mathematics, Basel, Birkhäuser, 1994, pp. 3–17.

    Google Scholar 

  5. J.T. Betts and W.P. Huffmann, “The application of sparse nonlinear programming to trajectory optimization,” J. of Guidance, Control and Dynamics, vol. 14, pp. 338–348, 1991.

    Google Scholar 

  6. N. Bourbaki, Integration, Ch. 9, Hermann: Paris, 1963.

  7. C. Büskens, “Optimierungsmethoden und Sensitivitätsanalyse für optimale Steuer-prozesse mit Steuer-und Zustandsbeschränkungen,” Dissertation, Universität Münster, Institut für Numerische Mathematik, Münster, Germany, 1998.

    Google Scholar 

  8. C. Büskens and H. Maurer, “SPQ-methods for solving optimal control problems with control and state constraints: Adjoint variables, sensitivity analysis and real-time control,” to appear in “SPQ-based direct discretisation methods for practical optimal control problems” (V. Schulz, ed.), J. Comp. Appl. Math., 2000.

  9. E. Casas, “Boundary control with pointwise state constraints,” SIAM J. Control Optim., vol. 31, pp. 993–1006, 1993.

    Google Scholar 

  10. E. Casas, F. Tröltzsch, and A. Unger, “Second order sufficient optimality conditions for a nonlinear elliptic control problem,” J. for Analysis and its Applications, vol. 15, pp. 687–707, 1996.

    Google Scholar 

  11. E. Casas, F. Tröltzsch, and A. Unger, “Second order sufficient optimality conditions for some state constrained control problems of semilinear elliptic equations,” Fakultät für Mathematik, Technische Universität Chemnitz, Preprint 97-19, to appear in SIAM J. Control Optim.

  12. R. Fourer, D.M. Gay, and B.W. Kernighan, AMPL: A Modeling Language for Mathematical Programming, Duxbury Press, Brooks-Cole Publishing Company, 1993.

  13. I.I. Grachev and Yu.G. Evtushenko, “A library of programs for solving optimal control problems,” U.S.S.R. Computational Maths. Math. Physics, vol. 19, pp. 99–119, 1979.

    Google Scholar 

  14. R. Hettich, A. Kaplan, and R. Tischatschke, “Regularized penalty methods for illposed optimal control problems with elliptic equations. Part I: Distributed control with bounded control set and state constraints,” Control and Cybernetics, vol. 26, pp. 5–27, 1997a.

    Google Scholar 

  15. R. Hettich, A. Kaplan, and R. Tischatschke, “Regularized penalty methods for illposed optimal control problems with elliptic equations. Part II: Distributed and boundary control with unbounded control sets and state constraints,” Control and Cybernetics, vol. 26, pp. 29–43, 1997b.

    Google Scholar 

  16. K. Ito and K. Kunisch, “Augmented Lagrangian-SQP methods for nonlinear optimal control problems of tracking type,” SIAM J. Optim., vol. 6, pp. 96–125, 1996.

    Google Scholar 

  17. D. Kraft, “On converting optimal control problems into nonlinear programming problems,” in Computational Mathematical Programming, K. Schittkowski (Ed.), NATO ASI Series F: Computer and Systems Science, vol. 15, Springer Verlag: Berlin und Heidelberg, 1985, pp. 261–280.

    Google Scholar 

  18. K. Kunisch and S. Volkwein, “Augmented Lagrangian-SQP techniques and their approximations,” Contemporary Mathematics, vol. 209, pp. 147–159, 1997.

    Google Scholar 

  19. J.L. Lions, “Optimal control of systems governed by partial differential equations,” Grundlehren der mathematischen Wissenschaften, vol. 170, Springer-Verlag: Berlin, New York, 1971.

    Google Scholar 

  20. J.L. Lions and E. Magenes, “Non-Homogeneous Boundary Value Problems and Applications, Volume I,” Grundlehren der mathematischen Wissenschaften, vol. 181, Springer-Verlag: Berlin, New York, 1972.

    Google Scholar 

  21. H.D. Mittelmann and P. Spellucci, “Decision Tree for Optimization Software,” World Wide Web, http://plato.la.asu.edu/guide.html (1998).

  22. K.L. Teo, C.J. Goh, and K.H. Wong, A Unified Computational Approach to Optimal Control Problems, Longman Scientific and Technical: New York, 1981.

    Google Scholar 

  23. R.S. Vanderbei and D.F. Shanno, “An interior point algorithm for nonconvex nonlinear programming,” Comput. Optim. Appl., vol. 13, pp. 231–252, 1999.

    Google Scholar 

  24. J. Zowe and S. Kurcyusz, “Regularity and stability for the mathematical programming problem in Banach spaces,” Appl. Math. Optimization, vol. 5, pp. 49–62, 1979.

    Google Scholar 

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Maurer, H., Mittelmann, H.D. Optimization Techniques for Solving Elliptic Control Problems with Control and State Constraints: Part 1. Boundary Control. Computational Optimization and Applications 16, 29–55 (2000). https://doi.org/10.1023/A:1008725519350

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