Explicit solution. Properties of vector norms

In summary, the conversation discusses an equation related to a system and its disturbance, and the speaker is looking for a way to manipulate the equation to obtain a simple solution for a specific variable. They have some knowledge about the components in the equation, such as the scaled matrix and the singular values, but are unsure how to rearrange the equation. After some discussion and research, the speaker is able to find a solution.
  • #1
james1234
19
0
I have an equation (constraint) which I wish to solve explicitly in terms of gd (or more precisely a scaling factor of the vector gd) but I am unsure how to manipulate the equation

σi(G) = |uiHgd|-1

Background: (please feel free to skips this. Much of this inforomation is I'm sure irrelevant):

Looking at the above equation, gd defines the maximum disturbance acting on a reduced order model of my system (G).

Here σ is a vector of the maximum singular values of the system (for those not familiar with singular values of transfer function matrices one might think of a plot of the singular values as simply the bode plot of a multivariable system which accounts for interaction between the respective channels (off diagonal terms))

G is my scaled system. In this instance G is simply a 4x4 transfer function matrix (4 inputs / 4 outputs)

uiH is the hermitian transpose (conjugate transpose) of the i'th column of the matrix U (where U has been obtained from the singular value decomposition G = UƩVH)

Finally, just to make clear the form of the 'disturbance'. gd is simply a column of the TF matrix Gd (corresponds to a single disturbance ~ input). I.e. while G is a 4x4 TF matrix which defines the magnitude and phase of the system between the outputs and control inputs of the system. Gd is the magnitude and phase between these same outputs and several disturbance inputs (perturbations to particular states) and has the same dimension as G.

Attempt at a solution:

As the system 'G' has already been scaled (the maximum input and output vector of the TF matrix have a euclidean norm of 1). I would now like to obtain a suitable scaling factor for the disturbance Gd which satisfies the above constraint

What I know ~
The scaled matrix Gd is equal to De-1*Gdunscaled*Dd, where De and Dd are the diagonal matrices used to scale Gdunscaled
the elements of De are known
The singular values of G, (σi(G)), are known
uiH is also known

Hence re-writting the above constraint I therefore have

σi(G) = |uiHDe-1gdDdi|-1

where Dd_i represents the element corresponding to the ith column of Gd and is the only unknown

What I would like to obtain.. σi(G) = |uiHDe-1gdDdi|-1

i(G)+1) = |uiH||De-1||gd||Ddi||gd|-1|De-1|-1|uiH|-1i(G)+1) = |Ddi|=di

Looking at the properties of the vector norm, I gather that the simple manipulation I have performed isn't pheasible and that the solution will not satisfy the initial constraint.
http://www.uAlberta.ca/MATH/gauss/fcm/LinAlg/InRn/DotPrdct/NrmPrprts.htm

If anyone can suggest a (simple! :)) approach for re-arranging the equation (keeping in mind that gd and ui are vectors ~ ui is a vector of complex numbers and gd is a vector of proper rational functions of j*omega) I would be most grateful. I am unaware of any method for manipulating the equation to obtain a simple solution for di.

Cheers!
 
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  • #2
Finally, resolved :rolleyes: Thanks to all who took a look.
I wanted to delete the post or mark it as solved to push it further down the 'list'. I don't appear to able to do either..

Cheers!
 

FAQ: Explicit solution. Properties of vector norms

1. What is an explicit solution?

An explicit solution is a mathematical expression that directly gives the value of a variable or equation without the need for further manipulation or iteration. It can also be referred to as a closed-form solution.

2. What are some properties of vector norms?

Some properties of vector norms include homogeneity, triangle inequality, and positive definiteness. Homogeneity means that multiplying a vector by a scalar will result in the norm being multiplied by the absolute value of the scalar. Triangle inequality means that the norm of the sum of two vectors is less than or equal to the sum of the norms of the individual vectors. Positive definiteness means that the norm of a vector is equal to zero only when the vector itself is equal to zero.

3. How are vector norms useful in scientific research?

Vector norms are useful in scientific research because they allow for the measurement of the magnitude or size of a vector. This is important in many fields, such as physics and engineering, where the magnitude of a vector can represent physical quantities such as force, velocity, or acceleration. Vector norms also allow for the comparison of vectors and can be used to determine the convergence or divergence of iterative processes.

4. Can vector norms be negative?

No, vector norms cannot be negative. The norm of a vector is always a non-negative value since it represents the magnitude or size of the vector. In some cases, the norm may be equal to zero, but it can never be negative.

5. Are all vector norms equivalent?

No, not all vector norms are equivalent. There are many different types of vector norms, such as Euclidean norm, Manhattan norm, and maximum norm, and they can produce different results for the same vector. However, all vector norms satisfy the three properties of homogeneity, triangle inequality, and positive definiteness.

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