Possible Expressions for Matrix Rotation Reduction

In summary, the conversation discusses the possibility of expressing the relationship between two coordinate systems using rotation matrices with time-dependent angles. The question is whether it is possible to express this relationship in a simplified form using a combination of rotation matrices. The conversation also touches on the definition of rotation matrices and whether the product of rotation matrices is always a rotation matrix.
  • #1
Deadstar
104
0
I'm wondering if the following is possible.

Consider some inertial coordiante system x, y, z, and a rotating coordiante system p, q, r defined through matrix rotations as follows.

[itex] \begin{pmatrix} p \\ q \\ r \end{pmatrix} = R_1(\theta_1(t)) R_2(\theta_2(t)) R_3(\theta_3(t)) \begin{pmatrix} x \\ y \\ z \end{pmatrix}[/itex]

Where this is simple a 1-2-3 rotation as described in the http://en.wikipedia.org/wiki/Rotation_matrix "In three dimensions" section, with the [itex]\theta[/itex] values being time dependant angles. I used 1,2,3 instead of x,y,z since I already have them as variables and 1,2,3 is more general to arbitrary coordiante systems. The above then is a 1-2-3 rotation but really any combination of rotation matrices can be used, this is just an example.

So let's now define a new coordinate system a,b,c, such that

[itex] \begin{pmatrix} a \\ b \\ c \end{pmatrix} = R_1(C_1) R_2(C_1) \begin{pmatrix} p \\ q \\ r \end{pmatrix}[/itex]

Where [itex]C_1[/itex] and [itex]C_2[/itex] are constants, again which rotation matrices are used doesn't really matter.

We could alternatively write this as.

[itex]\begin{pmatrix} a \\ b \\ c \end{pmatrix} = R_1(C_1) R_2(C_1) R_1(\theta_1(t)) R_2(\theta_2(t)) R_3(\theta_3(t)) \begin{pmatrix} x \\ y \\ z \end{pmatrix}[/itex]

To show the relationship between a,b,c and our inertial coordinate system.

Now my question is, is there anyway of expressing the above as

[itex]\begin{pmatrix} a \\ b \\ c \end{pmatrix} = R_i(\phi_1(t)) R_j(\phi_2(t)) R_k(\phi_3(t)) \begin{pmatrix} x \\ y \\ z \end{pmatrix}[/itex]

Where i, j, k can be 1,2 or 3 to denote which rotation matrix, and [itex]\phi[/itex] time dependant angles that will surely depends on the [itex]\theta[/itex] angles and the constants.

I would have thought yes since a,b,c's orientation is constant with respect to p,q,r, but I'm not sure what the above form would be.

Thanks for any info.
 
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  • #2
Deadstar said:
We could alternatively write this as.

[itex]\begin{pmatrix} a \\ b \\ c \end{pmatrix} = R_1(C_1) R_2(C_1) R_1(\theta_1(t)) R_2(\theta_2(t)) R_3(\theta_3(t)) \begin{pmatrix} x \\ y \\ z \end{pmatrix}[/itex]

To show the relationship between a,b,c and our inertial coordinate system.

Now my question is, is there anyway of expressing the above as

[itex]\begin{pmatrix} a \\ b \\ c \end{pmatrix} = R_i(\phi_1(t)) R_j(\phi_2(t)) R_k(\phi_3(t)) \begin{pmatrix} x \\ y \\ z \end{pmatrix}[/itex]

Can't you do it the trivial way?:

[itex] R_i(\phi_1(t)) = R_1(C_1) R_2(C_1) R_1(\theta_1(t)) [/itex]
[itex] R_j(\phi_2(t)) = R_2(\theta_2(t)) [/itex]
[itex] R_k(\phi_3(t)) = R_3(\theta_3(t)) [/itex]

Or are you defining "rotation matrix" in some way so that the product of rotations matrices is not necessarily a rotation matrix?
 

Related to Possible Expressions for Matrix Rotation Reduction

1. What is matrix rotation reduction?

Matrix rotation reduction is a mathematical process used to simplify a matrix by reducing its size and complexity while preserving its essential properties.

2. Why is matrix rotation reduction important?

Matrix rotation reduction can improve the efficiency of algorithms and calculations involving matrices, making them easier and faster to solve.

3. How is matrix rotation reduction performed?

Matrix rotation reduction involves applying mathematical operations, such as matrix multiplication and addition, to transform the original matrix into a simpler form.

4. What are the benefits of using matrix rotation reduction?

Some benefits of matrix rotation reduction include simplifying complex problems, reducing the amount of memory and computation needed, and improving the accuracy of results.

5. Can matrix rotation reduction be used on any type of matrix?

Yes, matrix rotation reduction can be used on any type of matrix, including square matrices, rectangular matrices, and even non-numerical matrices such as graphs and networks.

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