
Elements Of Electromagnetics
7th Edition
ISBN: 9780190698614
Author: Sadiku, Matthew N. O.
Publisher: Oxford University Press
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Can you use MATLAB?
![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 (Ax + Bu)dl + Gdw,
d = f(x, u, t)dt + Gdw,
(1)
(2)
where 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. I.
Problem Set 9 Linear Stochastic Process
In this problem, we consider the linear SDE, Eq. (1), with a very simple model where x = R², u = [0,0]T
(no control), and dw€ R². The matrices A, B, and G are given as follows:
A-02x2, B-02x2, G=
(3)
where σp E IR represents the degree of the uncertainty, and let us take o₁ = 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
tЄ [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 a over time; include 3-0
bounds (i.e., ±30) in the plot and discuss the consistency.](https://content.bartleby.com/qna-images/question/ad0d55fe-d83b-4711-86a1-cee8ecea510f/43bf2311-df4f-43ee-8795-e2e57cfe407a/9bosyye_thumbnail.png)
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 (Ax + Bu)dl + Gdw,
d = f(x, u, t)dt + Gdw,
(1)
(2)
where 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. I.
Problem Set 9 Linear Stochastic Process
In this problem, we consider the linear SDE, Eq. (1), with a very simple model where x = R², u = [0,0]T
(no control), and dw€ R². The matrices A, B, and G are given as follows:
A-02x2, B-02x2, G=
(3)
where σp E IR represents the degree of the uncertainty, and let us take o₁ = 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
tЄ [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 a over time; include 3-0
bounds (i.e., ±30) in the plot and discuss the consistency.
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