Convolution integral with e^At

In summary, the conversation was about solving non-homogeneous state space equations in a Dynamic Systems and Controls course for Mechanical Engineering. The question was about a 2x2 state space differential equation and whether the simplifications made by assuming e^At is only real numbers (without sine or cosine terms) always hold true. The answer is yes, as long as A is a real symmetric or Hermitian matrix. This can be shown by writing e^At in terms of its eigenvectors and using the properties of real symmetric matrices. However, this may not hold true for other types of matrices.
  • #1
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Hi All,

I am taking Dynamic Systems and Controls this semester for Mechanical Engineering. We are solving non homogeneous state space equations right now. This question is about a 2x2 state space differential equation that takes the form:

8dB1Lxa.png

Where A and B are matrices, while u is an input (like sin(t), etc). We have written this to generally be:

R7dFLwG.png

The situation I am wondering about, is when e^At is only real numbers (we use the laplace transform to solve this normally, so no sine or cosine), which means it is of the form:

04a2P3e.png

Our professor agrees that C1*C2 = 0 is always true when it is of this form, which simplifies the convolution integral significantly. Additionally, in homeworks and lecture notes I noticed that C1*C1 = C1 and C2*C2=C2. I asked the Professor if this is always true, and he commented that he's not sure if it is always but that it was for the examples we did. What do you guys think, is that always true? If so, that simplifies the convolution integral even further. I am an engineer, not a mathematician, so my memory of DiffEq and proofs isn't so good, but I thought this was an interesting question
 
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  • #2
It's true provided [itex]\mathbf{A}[/itex] is a real symmetric matrix ([itex]\mathbf{A} = \mathbf{A}^T[/itex]), or more generally a Hermitian matrix. Ideally you should understand at least some of why that is. If [itex]\mathbf{A}[/itex] is a real symmetric matrix, then it has an orthogonal basis of eigenvectors, and we may write
[tex]\mathbf{A} = \beta_1vv^T + \beta_2ww^T [/tex]
Where [itex]v[/itex] and [itex]w[/itex] are vectors such that

[itex]v^Tv = w ^T w = 1[/itex]
[itex]v^Tw = w^Tv = 0[/itex]

You should verify that this means [itex]\mathbf{A}v = \beta_1 v [/itex] and [itex]\mathbf{A}w = \beta_2 w [/itex]. Furthermore, due to the properties above,

[itex]\mathbf{A}^2 = \beta_1^2vv^T + \beta_2^2ww^T [/itex]
[itex]\mathbf{A}^n = \beta_1^nvv^T + \beta_2^nww^T [/itex]

Now
[tex]e^{\mathbf{A}t} = \sum^\infty_{n = 0} \frac{\mathbf{A}^nt^n}{n!}= 1 + \mathbf{A}t + \frac{1}{2}\mathbf{A}\mathbf{A}t^2 + ...[/tex]
So using our formula from above,
\begin{align*} e^{\mathbf{A}t} &= \sum^\infty_{n = 0} \frac{t^n}{n!}\beta_1^nvv^T + \frac{t^n}{n!}\beta_2^nww^T \\
&= vv^T\, e^{\beta_1 t} + ww^T \, e^{\beta_2 t} \end{align*}

So, [itex]\mathbf{C}_1 = vv^T[/itex] and [itex]\mathbf{C}_2 = ww^T[/itex], and indeed
\begin{align*}\mathbf{C}_1\mathbf{C}_2 &= v(v^Tw)w^T = v0w^T = 0 \\
\mathbf{C}_1\mathbf{C}_1 &= v(v^Tv)v^T = v(1)v^T = \mathbf{C}_1 \\
\mathbf{C}_2\mathbf{C}_2 &= w(w^Tw)w^T = w(1)w^T = \mathbf{C}_2 \\
\end{align*}
But, remember we had to assume that [itex]\mathbf{A}[/itex] had a complete set of orthonormal eigenvectors, [itex]\mathbf{A} = \beta_1vv^T + \beta_2ww^T [/itex]. This is true for a real symmetric matrix, which means [itex]\mathbf{A} = \mathbf{A}^T[/itex]. For other kinds of matrices this may not hold. You will not even generally be able to write [itex]e^{\mathbf{A}t} = C_1 e^{\beta_1t} + C_2 e^{\beta_2t}[/itex] for other kinds of matrices.
 

What is the convolution integral with e^At?

The convolution integral with e^At is a mathematical operation that combines two functions to create a third function. It is commonly used in the field of signal processing and is represented by the symbol *.

How is the convolution integral with e^At calculated?

The convolution integral with e^At is calculated by integrating the product of two functions, one of which is the exponential function e^At. The integral is taken with respect to the variable t and the result is a new function that describes the combined effect of the original two functions.

What is the significance of e^At in the convolution integral?

The exponential function e^At plays a crucial role in the convolution integral as it allows for the representation of systems with exponential behavior. It is often used to describe the response of systems to external stimuli, such as in the case of an RC circuit.

What are the applications of the convolution integral with e^At?

The convolution integral with e^At has a wide range of applications in various fields such as engineering, physics, and finance. It is used to model various physical systems, analyze signals, and solve differential equations. It also has applications in probability theory and statistics.

Are there any limitations to the convolution integral with e^At?

One limitation of the convolution integral with e^At is that it assumes linearity and time-invariance in the system being studied. This may not always hold true in real-world scenarios, leading to errors in the calculated results. Additionally, the integration process can be complex and time-consuming, making it difficult to use for more complicated systems.

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