We will consider a linear system and a nonlinear system under uncertainty, each expressed in the form of a set of stochastic differential equation (SDE) as follows: da (Ar+ Bu)dt+ Gdw, da f(x,u,t)dt+Gdw, (1) ཌས (2) where x is the state, u is the control, and dw is a differential increment of standard Brownian motion, i.e., E[dw] = 0 and E[dw(t)dw(t)] = dt-1. Problem Set 9 Linear Stochastic Process In this problem, we consider the linear SDE, Eq. (1), with a very simple model where 2 € R², u = [0,0] (no control), and dw R². The matrices A, B, and G are given as follows: A=02x2, B=02x2, G= [002 (3) where σp E R represents the degree of the uncertainty, and let us take ₁ = 2 and 2-3. Assume that the initial state is deterministic and (t = 0) = [0,0]. Take the following steps to simulate the given SDE for t€ [0, 1]: Perform Monte Carlo simulation (again M = 20) by propagating the linear SDE with the approx- imated Brownian motion, and show the time history of each element of over time; include 3-0 bounds (i.e., ±30) in the plot and discuss the consistency.

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Chapter5: Analysis Of Convection Heat Transfer
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Problem 5.12P
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We will consider a linear system and a nonlinear system under uncertainty, each expressed in the form
of a set of stochastic differential equation (SDE) as follows:
da (Ar+ Bu)dt+ Gdw,
da f(x,u,t)dt+Gdw,
(1)
ཌས
(2)
where x is the state, u is the control, and dw is a differential increment of standard Brownian motion, i.e.,
E[dw] = 0 and E[dw(t)dw(t)] = dt-1.
Problem Set 9 Linear Stochastic Process
In this problem, we consider the linear SDE, Eq. (1), with a very simple model where 2 € R², u = [0,0]
(no control), and dw R². The matrices A, B, and G are given as follows:
A=02x2, B=02x2, G= [002
(3)
where σp E R represents the degree of the uncertainty, and let us take ₁ = 2 and 2-3. Assume that the
initial state is deterministic and (t = 0) = [0,0]. Take the following steps to simulate the given SDE for
t€ [0, 1]:
Perform Monte Carlo simulation (again M = 20) by propagating the linear SDE with the approx-
imated Brownian motion, and show the time history of each element of over time; include 3-0
bounds (i.e., ±30) in the plot and discuss the consistency.
Transcribed Image Text:We will consider a linear system and a nonlinear system under uncertainty, each expressed in the form of a set of stochastic differential equation (SDE) as follows: da (Ar+ Bu)dt+ Gdw, da f(x,u,t)dt+Gdw, (1) ཌས (2) where x is the state, u is the control, and dw is a differential increment of standard Brownian motion, i.e., E[dw] = 0 and E[dw(t)dw(t)] = dt-1. Problem Set 9 Linear Stochastic Process In this problem, we consider the linear SDE, Eq. (1), with a very simple model where 2 € R², u = [0,0] (no control), and dw R². The matrices A, B, and G are given as follows: A=02x2, B=02x2, G= [002 (3) where σp E R represents the degree of the uncertainty, and let us take ₁ = 2 and 2-3. Assume that the initial state is deterministic and (t = 0) = [0,0]. Take the following steps to simulate the given SDE for t€ [0, 1]: Perform Monte Carlo simulation (again M = 20) by propagating the linear SDE with the approx- imated Brownian motion, and show the time history of each element of over time; include 3-0 bounds (i.e., ±30) in the plot and discuss the consistency.
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