guidance, flight mechanics and trajectory optimization
guidance, flight mechanics and trajectory optimization
guidance, flight mechanics and trajectory optimization
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of the p <strong>and</strong> $ equations (i.e., Eqs. (2.5.106A) <strong>and</strong> (2.5.111) with the<br />
initial condition of Eq. (2.5.112B), the terminal condition of Eq. (2.5.106~)<br />
<strong>and</strong> with A selected so that Ptt,) satisfies the terminal variance<br />
constraint which is imposed.<br />
In.most cases, the solution will have to be achieved iteratively.<br />
Thus, the process might proceed as follows:<br />
(1) Guess the diagonal matrix A . As has been noted, the number of<br />
independent diagonal elements (i.e., the number of different<br />
quantities that can be guessed) is equal to the number of terminal<br />
constraints imposed. For example, if Eq. (2.5.87A) is used,<br />
then only one constraint is imposed <strong>and</strong> all the diagonal elements<br />
of -A are equal to some number, say A-. This number would be<br />
guessed to start the iteration.<br />
(2) Integrate the equation for Zi backwards in time with s (&+,) = n<br />
C i.e., integrate Eq. (2.5.106A) 1 .<br />
(3) Set p&J = ;,;z<strong>and</strong> integrate the P equation forward from<br />
*, to t, [i.e., Eq. (2.5.111) I.<br />
(4) Test P 14)to see if the specified terminal constraints are<br />
satisfied.<br />
(5) ze;hT2;onstraints are not satisfied, adjust h <strong>and</strong> go back to<br />
.<br />
Since the terminal constraints are inequality constraints [see<br />
Eq. (2.5.87)] , this iteration scheme will not lead to a unique solution.<br />
However, it can be shown, using st<strong>and</strong>ard methods from the Calculus of<br />
Variations, that A must be a negative semi-definite matrix, with the<br />
diagonal elements all less than or equal to zero. This condition<br />
suggests that the iteration loop above should start with the condition<br />
nzo; furthermore, it generally allows for a unique solution to the<br />
iteration problem.<br />
Summarizing the results for the perfectly observable case, the optimal<br />
feedback control is given by Eq, (2.5.107) where the matrix s is determined<br />
from Eq. (2.5.106A). The Lagrange multiplier matrix A is selected so that<br />
the simultaneous solution of Eq. (2.5.106A) <strong>and</strong> (2.5.111) lead to a control<br />
which satisfies the specified terminal constraints.<br />
2.5.3.2 Perfectly Inobservable Case<br />
The treatment of the, perfectly inobservable case parallels that given<br />
in Section (2.5.2.2) where no terminal conditions were imposed. Again,<br />
the problem is to minimize the performance index<br />
140<br />
(2.5.114)