next up previous
Next: Conclusions Up: Examples and numerical Previous: Optimal ascent of

A problem with a second order state variable inequality constraint

This well-known problem is due to Bryson, Denham, and Dreyfus [3]. After a transformation, the differential equations and boundary conditions are

The objective is

The state constraint to be taken into account is of order 2 here

Explicit formulae of the solution depending on the value of l can be given, cf. [3], [4]. For , there exists an interior boundary arc = = where the state constraint is active. The minimum objective value is . With the Hamiltonian the minimum principle yields for the adjoint variables

and . The adjoint variable suffers discontinuities when entering or leaving the state constraint. A first solution is obtained by using DIRCOL with an equidistant grid of N=11 grid points resulting in a minimum objective value of . In Figs. 7 to 10 these first suboptimal solutions are shown by dashed lines and the exact solutions are shown by solid lines. In addition, the grid points of the discretization are marked.
*[-1.6cm] =5.2cm =7.3cm ZZZG67_x.epsf =5.2cm =7.3cm ZZZG67_a.epsf
*[0.3cm] Fig. 7: The state variable x. Fig. 8: The control variable u.

*[-3.0cm] =5.2cm =7.3cm ZZZG67_lambda_x.epsf =5.2cm =7.3cm ZZZG67_lambda_v.epsf
*[0.3cm] Fig. 9: The adjoint variable . Fig. 10: The adjoint variable .

The solution is now refined by using a ``three-stage'' collocation approach that includes the switching structure of the state constraint, i. e. the switching points and are included as two additional parameters with two additional equality conditions in the optimization procedure

The method DIRCOL is now applied to the reformulated problem with a separate grid of 4 grid points in each of the three stages , , and . This results in a minimum objective value of and a more accurately satisfied state constraint. In Figs. 11 to 14 the refined solutions are shown. In addition, two dotted vertical lines show the entry and exit points of the state constraint that are computed with an error of one percent. The quality of the estimated adjoint variables and also of the control variable has been significantly improved while the dimension of the resulting nonlinear program has not been increased.

*[-1.6cm] =5.2cm =7.3cm ZZZG67_x3.epsf =5.2cm =7.3cm ZZZG67_a3.epsf
*[0.3cm] Fig. 11: The state variable x. Fig. 12: The control variable u.

=5.2cm =7.3cm ZZZG67_lambda_x3.epsf =5.2cm =7.3cm ZZZG67_lambda_v3.epsf
*[0.3cm] Fig. 13: The adjoint variable . Fig. 14: The adjoint variable .

next up previous
Next: Conclusions Up: Examples and numerical Previous: Optimal ascent of

Oskar von Stryk
Fri Apr 5 21:38:03 MET DST 1996