Math 2.0

Calculus of variations and optimal control; optimization Definition

Hamiltonian of an optimal control problem

Builds on Optimal controlControl system

Definition

Let U⊆ℝmU \subseteq \R^m be a nonempty set and I⊆ℝI \subseteq \R an interval. Consider an optimal control problem with control system x′=f(t,x,u)x' = f(t, x, u) and running cost ∫L(t,x,u) dt\int L(t, x, u) \, dt, where f ⁣:I×ℝn×U→ℝnf \colon I \times \R^n \times U \to \R^n and L ⁣:I×ℝn×U→ℝL \colon I \times \R^n \times U \to \R. The Hamiltonian of the problem is the function H ⁣:I×ℝn×U×ℝn×ℝ→ℝH \colon I \times \R^n \times U \times \R^n \times \R \to \R given by H(t,x,u,p,λ0)=p⋅f(t,x,u)−λ0L(t,x,u).H(t, x, u, p, \lambda_0) = p \cdot f(t, x, u) - \lambda_0 L(t, x, u). The vector p∈ℝnp \in \R^n is the costate, and the number λ0≥0\lambda_0 \ge 0 is the cost multiplier. In the normal case λ0=1\lambda_0 = 1 one writes H(t,x,u,p)=p⋅f(t,x,u)−L(t,x,u)H(t, x, u, p) = p \cdot f(t, x, u) - L(t, x, u).

Remarks

Example: for x′=ux' = u in ℝ\R with running cost ∫12(x2+u2) dt\int \frac12 (x^2 + u^2) \, dt, the normal Hamiltonian is H=pu−12(x2+u2)H = p u - \frac12 (x^2 + u^2). For fixed (x,p)(x, p) it is a concave function of uu with maximum at u=pu = p. The state equation is recovered as x′=∂H/∂px' = \partial H / \partial p. Many texts use the opposite sign convention H=L+p⋅fH = L + p \cdot f, under which optimal controls minimize HH; this gives the minimum principle. The two conventions differ by the change p↦−pp \mapsto -p and an overall sign. A non-example is the Lagrangian L(t,x,u)L(t, x, u) alone: it does not involve the costate, so it does not carry the dynamics.

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Further reading

  1. Web https://en.wikipedia.org/wiki/Hamiltonian_(control_theory)
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TagENA9XD5B77
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Kinddefinition
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MSC49K15, 93C15
DefinesHamiltonian of an optimal control problem
UsesOptimal control, Control system

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