L10-StochOptControl

J t x min w x u t dt j t dt xt dt u where

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Unformatted text preview: ith a corresponding distribution of costs from which we can calculate an expected cost. J (t , x ) = min w (x , u , t )dt + J (t + dt , xt +dt ) u Where the expectation occurs because, while we know our location and control precisely, we do not know precisely where this will take us. I’ll discuss u (x , t ) here. Even though we’ve precomputed it but it’s useful when buffeted by (specifically unanticipated but generally modeled) noise. Nick Jones Elements of Stochastic Optimal Control: ICDNS Stochastic Hamilton Jacobi Bellman equation J (t , x ) = min w (x , u , t )dt + J (t + dt , xt +dt ) u We can Taylor expand J (t + dt , xt +dt ). We have to go to second order in dx since < dx 2 >= O(dt ) (basics of SDEs). J (t +dt , xt +dt ) = J (x , t )+dt ∂t J (t , x )+ dx ∂x J (t , x )+ 1 2 dx 2 ∂x J (t , x ) 2 Noting that < dx >= f (x , u , t )dt and < dx 2 >= ν (t , x , u )dt we obtain: 1 2 −∂t J (t , x ) = min(w (x , u , t ) + f (x , u , t )∂x J (t , x ) + ν (t , x , u )∂x J (t , x )) u 2 This is the Stochastic HJB equation. We have picked up a diffusion-like term in our dynamics. Nick Jones Elements of Stochastic Optimal Control: ICDNS Linear Quadratic Gaussian Control Linear dynamics, quadratic costs, Gaussian (white) noise. Linear Dynamics with Gaussian noise: dx = [Ax + Bu ]dt + Fd ξ Quadratic Cost: 1 1 w (x , u , t ) = u T Ru + x T Qx 2 2 Final Cost: 1 W (x ) = x T QT x 2 Where we can have A, B , F , R , Q time varying. Nick Jones Elements of Stochastic Optimal Control: ICDNS Linear Quadratic Gaussian Control II One can show (using the stochastic HJB) that the optimal cost-to-go is of the form: 1 J (x , t ) = x T V (t )x + a(t ) 2 (with V symmetric) and the corresponding optimal control: u (x , t ) = −R −1 B T...
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