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## 3.4.3 Second variation

To better understand the behavior of as a function of along the optimal trajectory, let us bring in the second variation. To do this, we must first augment the description (3.25) of the perturbed state trajectory with the second-order term in , writing We then need to go back to the expressions (3.32)-(3.34) and expand them by adding second-order terms in . With already set equal to as defined above, it is relatively straightforward to check that the -dependent terms drop out--exactly in the same way as the -dependent terms dropped out of the first variation (3.35)--and that the second variation is given by

 (3.40)

where we recall that is obtained as the state of the system (3.27) driven by with the initial condition .

We know from the second-order necessary condition for optimality (see Section 1.3.3) that we must have for all . Let us concentrate on the integrand in (3.41) and ask the following question: does one term in the Hessian matrix of dominate the other terms? If yes, then this term should be nonpositive to ensure the correct sign of . We encountered a very similar issue in Section 2.6.1 in relation to the inequality (2.61). We found there that for the overall integral to be nonnegative, the function multiplying must be nonnegative, because may be large while itself is small. The present situation is a bit different since is not just the derivative of , i.e., the system relating the two is not a simple integrator. However, the corresponding conclusion is still valid: is the dominant term because we may have a large producing a small (but not vice versa), as illustrated in Figure 3.5. Thus, in order for the second variation (3.41) to be nonnegative, we must have for all , which can only happen if the matrix is negative semidefinite:

 (3.41)

This condition is known as the Legendre-Clebsch condition (in its Hamiltonian formulation).

We already know from (3.39) that for each , the function must have a stationary point at . Now the Legendre-Clebsch condition (3.42) tells us that if this stationary point is an extremum, then it is necessarily a maximum. Even though we have not proved that the stationary point must indeed be an extremum, it is tempting to conjecture that this Hamiltonian maximization property is true. In other words, our findings up to this point are very suggestive of the following (not yet proved) necessary conditions for optimality: If is an optimal control and is the corresponding optimal state trajectory, then there exists an adjoint vector (costate) such that:

1. and satisfy the canonical equations (3.40) with the boundary conditions , .

2. For each fixed , the function has a (local) maximum at :

near

Next: 3.4.4 Some comments Up: 3.4 Variational approach to Previous: 3.4.2 First variation   Contents   Index
Daniel 2010-12-20