The really interesting practical issue now is understanding how to compute an optimal control α^∗(.).

DEFINITION. We define K(t, x0) to be the reachable set for time t. That is,  K(t, x⁰) = {x¹| there exists α(.) ∈ A which steers from x⁰ to x¹at time t}.

Since x(.) solves (ODE), we have x¹ ∈ K(t, x⁰) if and only if

for some control α(.) ∈ A.

THEOREM 1.1 (GEOMETRY OF THE SET K). The set K(t, x0) is convex and closed.

Proof. 1. (Convexity) Let x¹, x² ∈ K(t, x⁰). Then there exists α₁,α₂ ∈ A such that

and hence λx¹ + (1 − λ)x² ∈ K(t, x⁰).

2. (Closedness) Assume x^k ∈ K(t, x⁰) for (k = 1, 2, . . . ) and x^k → y. We must show y ∈ K(t, x⁰). As x^k ∈ K(t, x⁰), there exists α_k(.) ∈ A such that

According to Alaoglu’s Theorem, there exist a subsequence k_j → ∞ and α ∈ Asuch that α_k^∗⇀α. Let k = k_j → ∞ in the expression above, to find

Thus y ∈ K(t, x⁰), and hence K(t, x⁰) is closed.

NOTATION. If S is a set, we write ∂S to denote the boundary of S.

Recall that τ^∗ denotes the minimum time it takes to steer to 0, using the optimal control α^∗. Note that then 0 ∈ ∂K(τ^∗, x0).

THEOREM 1.2 (PONTRYAGIN MAXIMUM PRINCIPLE FOR LINEAR TIME-OPTIMAL CONTROL). There exists a nonzero vector h such that

for each time 0 ≤ t ≤ τ^∗.

INTERPRETATION. The significance of this assertion is that if we know h then the maximization principle (M) provides us with a formula for computing α^∗(.), or at least extracting useful information.

We will see in the next chapter that assertion (M) is a special case of the general Pontryagin Maximum Principle.

Proof. 1. We know 0 ∈ ∂K(τ ^∗, x⁰). Since K( ^∗, x⁰) is convex, there exists a supporting plane to K(τ^∗, x⁰) at 0; this means that for some g = 0, we have

                              g. x₁ ≤ 0 for all x₁ ∈ K(τ^∗, x⁰ ).

2. Now x₁ ∈ K(τ^∗, x⁰) if and only if there exists α(.) ∈ A such that

Since g . x¹ ≤ 0, we deduce that

for all controls α(.) ∈ A.

3. We claim now that the foregoing implies

for almost every time s.

For suppose not; then there would exist a subset E ⊂ [0, τ^∗] of positive measure,  such that

for s ∈ E. Design a new control αˆ (.) as follows:

where α(s) is selected so that

This contradicts Step 2 above.

For later reference, we pause here to rewrite the foregoing into different notation;  this will turn out to be a special case of the general theory developed later in Chapter

4. First of all, define the Hamiltonian

                                 H(x, p, a) := (Mx + Na) .p            (x, p ∈ Rⁿ, a ∈ A).

THEOREM 1.3 (ANOTHER WAY TO WRITE PONTRYAGIN MAXIMUM PRINCIPLE FOR TIME-OPTIMAL CONTROL). Let α^∗(.) be a time

optimal control and x^∗(.) the corresponding response.

Then there exists a function p^∗(.) : [0, τ^∗] → Rⁿ, such that

We call (ADJ) the adjoint equations and (M) the maximization principle. The function p^∗(.) is the costate.

Proof. 1. Select the vector h as in Theorem 1.2, and consider the system

The solution is

and hence

2. We know from condition (M) in Theorem 1.2 that

3. Finally, we observe that according to the definition of the Hamiltonian H,

the dynamical equations for x^∗(.), p^∗(.) take the form (ODE) and (ADJ), as stated in the Theorem.

References

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مواضيع ذات صلة

DYNAMIC PROGRAMMING-DYNAMIC PROGRAMMING AND THE PONTRYAGIN MAXIMUM PRINCIPLE

DYNAMIC PROGRAMMING-EXAMPLES

DERIVATION OF BELLMAN,S PDE-DYNAMIC PROGRAMMING

THE PONTRYAGIN MAXIMUM PRINCIPLE-MORE APPLICATIONS

THE PONTRYAGIN MAXIMUM PRINCIPLE-MAXIMUM PRINCIPLE WITH STATE CONSTRAINTS

THE PONTRYAGIN MAXIMUM PRINCIPLE-MORE APPLICATIONS

THE PONTRYAGIN MAXIMUM PRINCIPLE-MAXIMUMPRINCIPLEWITH TRANSVERSALITY CONDITIONS

THE PONTRYAGIN MAXIMUM PRINCIPLE-APPLICATIONS AND EXAMPLES

THE PONTRYAGIN MAXIMUM PRINCIPLE-STATEMENT OF PONTRYAGIN MAXIMUM PRINCIPLE

THE PONTRYAGIN MAXIMUM PRINCIPLE-REVIEW OF LAGRANGE MULTIPLIERS.

THE PONTRYAGIN MAXIMUM PRINCIPLE-CALCULUS OF VARIATIONS, HAMILTONIAN DYNAMICS

LINEAR TIME-OPTIMAL CONTROL-EXAMPLES