Lyapunov stability of 4x4 matrix.
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Consider the following continuous-time state space representation of the form:
$frac{d}{dx}x(t) = Ax(t)+Bu(t), quad y(t)=Cx(t), quad tin mathbb{R}^{+}$
$A=begin{bmatrix}-1&3&0&0\-3&-1&0&0\0&0&0&3\0&0&-3&0 end{bmatrix} quad B = begin{bmatrix}0\1\0\0 end{bmatrix} quad C=begin{bmatrix}1&0&0&1 end{bmatrix}$
The corresponding eigenvalues are: $-1+3i, -1-3i, 0+3i text{and} 0-3i$.
The answer states that this system is Lyaponov stable.
But I'm wondering why.
Is it because the Jordan blocks of the eigenvalues with zero real-part are $1$x$1$. Because this matrix is in Jordan Form?
control-theory linear-control
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down vote
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Consider the following continuous-time state space representation of the form:
$frac{d}{dx}x(t) = Ax(t)+Bu(t), quad y(t)=Cx(t), quad tin mathbb{R}^{+}$
$A=begin{bmatrix}-1&3&0&0\-3&-1&0&0\0&0&0&3\0&0&-3&0 end{bmatrix} quad B = begin{bmatrix}0\1\0\0 end{bmatrix} quad C=begin{bmatrix}1&0&0&1 end{bmatrix}$
The corresponding eigenvalues are: $-1+3i, -1-3i, 0+3i text{and} 0-3i$.
The answer states that this system is Lyaponov stable.
But I'm wondering why.
Is it because the Jordan blocks of the eigenvalues with zero real-part are $1$x$1$. Because this matrix is in Jordan Form?
control-theory linear-control
1
Your $A$ matrix in not in Jordan form, but in its real Jordan form, so you have to look at the size of each full real Jordan block.
– Kwin van der Veen
2 days ago
I thought that a 4x4 real jordan form had an identity matrix in the upper right corner? like this:$begin{bmatrix}-1&3&1&0\-3&-1&0&1\0&0&0&3\0&0&-3&0 end{bmatrix}$
– user463102
2 days ago
1
Yes, only your example is not a real Jordan block, because then both 2x2 matrices on the diagonal need to be the same.
– Kwin van der Veen
2 days ago
Thank you. I did not know that a right upper 2x2 identity only appears if both 2x2 matrices on the diagonal are the same.
– user463102
yesterday
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
Consider the following continuous-time state space representation of the form:
$frac{d}{dx}x(t) = Ax(t)+Bu(t), quad y(t)=Cx(t), quad tin mathbb{R}^{+}$
$A=begin{bmatrix}-1&3&0&0\-3&-1&0&0\0&0&0&3\0&0&-3&0 end{bmatrix} quad B = begin{bmatrix}0\1\0\0 end{bmatrix} quad C=begin{bmatrix}1&0&0&1 end{bmatrix}$
The corresponding eigenvalues are: $-1+3i, -1-3i, 0+3i text{and} 0-3i$.
The answer states that this system is Lyaponov stable.
But I'm wondering why.
Is it because the Jordan blocks of the eigenvalues with zero real-part are $1$x$1$. Because this matrix is in Jordan Form?
control-theory linear-control
Consider the following continuous-time state space representation of the form:
$frac{d}{dx}x(t) = Ax(t)+Bu(t), quad y(t)=Cx(t), quad tin mathbb{R}^{+}$
$A=begin{bmatrix}-1&3&0&0\-3&-1&0&0\0&0&0&3\0&0&-3&0 end{bmatrix} quad B = begin{bmatrix}0\1\0\0 end{bmatrix} quad C=begin{bmatrix}1&0&0&1 end{bmatrix}$
The corresponding eigenvalues are: $-1+3i, -1-3i, 0+3i text{and} 0-3i$.
The answer states that this system is Lyaponov stable.
But I'm wondering why.
Is it because the Jordan blocks of the eigenvalues with zero real-part are $1$x$1$. Because this matrix is in Jordan Form?
control-theory linear-control
control-theory linear-control
edited 2 days ago
asked 2 days ago
user463102
14213
14213
1
Your $A$ matrix in not in Jordan form, but in its real Jordan form, so you have to look at the size of each full real Jordan block.
– Kwin van der Veen
2 days ago
I thought that a 4x4 real jordan form had an identity matrix in the upper right corner? like this:$begin{bmatrix}-1&3&1&0\-3&-1&0&1\0&0&0&3\0&0&-3&0 end{bmatrix}$
– user463102
2 days ago
1
Yes, only your example is not a real Jordan block, because then both 2x2 matrices on the diagonal need to be the same.
– Kwin van der Veen
2 days ago
Thank you. I did not know that a right upper 2x2 identity only appears if both 2x2 matrices on the diagonal are the same.
– user463102
yesterday
add a comment |
1
Your $A$ matrix in not in Jordan form, but in its real Jordan form, so you have to look at the size of each full real Jordan block.
– Kwin van der Veen
2 days ago
I thought that a 4x4 real jordan form had an identity matrix in the upper right corner? like this:$begin{bmatrix}-1&3&1&0\-3&-1&0&1\0&0&0&3\0&0&-3&0 end{bmatrix}$
– user463102
2 days ago
1
Yes, only your example is not a real Jordan block, because then both 2x2 matrices on the diagonal need to be the same.
– Kwin van der Veen
2 days ago
Thank you. I did not know that a right upper 2x2 identity only appears if both 2x2 matrices on the diagonal are the same.
– user463102
yesterday
1
1
Your $A$ matrix in not in Jordan form, but in its real Jordan form, so you have to look at the size of each full real Jordan block.
– Kwin van der Veen
2 days ago
Your $A$ matrix in not in Jordan form, but in its real Jordan form, so you have to look at the size of each full real Jordan block.
– Kwin van der Veen
2 days ago
I thought that a 4x4 real jordan form had an identity matrix in the upper right corner? like this:$begin{bmatrix}-1&3&1&0\-3&-1&0&1\0&0&0&3\0&0&-3&0 end{bmatrix}$
– user463102
2 days ago
I thought that a 4x4 real jordan form had an identity matrix in the upper right corner? like this:$begin{bmatrix}-1&3&1&0\-3&-1&0&1\0&0&0&3\0&0&-3&0 end{bmatrix}$
– user463102
2 days ago
1
1
Yes, only your example is not a real Jordan block, because then both 2x2 matrices on the diagonal need to be the same.
– Kwin van der Veen
2 days ago
Yes, only your example is not a real Jordan block, because then both 2x2 matrices on the diagonal need to be the same.
– Kwin van der Veen
2 days ago
Thank you. I did not know that a right upper 2x2 identity only appears if both 2x2 matrices on the diagonal are the same.
– user463102
yesterday
Thank you. I did not know that a right upper 2x2 identity only appears if both 2x2 matrices on the diagonal are the same.
– user463102
yesterday
add a comment |
1 Answer
1
active
oldest
votes
up vote
1
down vote
accepted
Write $I_n$ for a $n times n$ identity matrix.
Take $P = (1/2)I_4$ and $V(x) = x^T P x$, which is clearly positive definite. Now calculate the directional derivative:
$$
begin{align}
dot{V}(x) &= dot{x}^T P x + x^T P dot{x} \
&= x^T A^T P x + x^T P A x \
&= x^T(A^T P + P A) x \
&= x^T Q x,
end{align}
$$
insert $A$ and $P$ and derive $Q$:
$$
begin{align}
Q = A^T P + P A &=
begin{bmatrix}
-1 & -3 & 0 & 0 \
3 & -1 & 0 & 0 \
0 & 0 & 0 & -3 \
0 & 0 & 3 & 0
end{bmatrix} frac{1}{2} I_4 + frac{1}{2} I_4
begin{bmatrix}
-1 & 3 & 0 & 0 \
-3 & -1 & 0 & 0 \
0 & 0 & 0 & 3 \
0 & 0 & -3 & 0
end{bmatrix} \ &=
begin{bmatrix}
-1 & 0 & 0 & 0 \
0 & -1 & 0 & 0 \
0 & 0 & 0 & 0 \
0 & 0 & 0 & 0 end{bmatrix} .
end{align}
$$
So your directional derivative is $dot{V}(x) = -x_1^2 - x_2^2$, which is negative semi-definite. Therefore, the system is Lyapunov stable (but not asymptotically Lyapunov stable).
1
In this case it might be even easier to just look at the eigenvalues, because they all have an algebraic multiplicity of one. Only if the algebraic multiplicity of an eigenvalue with zero real part is higher than one, you also need to check its geometric multiplicity.
– Kwin van der Veen
2 days ago
add a comment |
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
1
down vote
accepted
Write $I_n$ for a $n times n$ identity matrix.
Take $P = (1/2)I_4$ and $V(x) = x^T P x$, which is clearly positive definite. Now calculate the directional derivative:
$$
begin{align}
dot{V}(x) &= dot{x}^T P x + x^T P dot{x} \
&= x^T A^T P x + x^T P A x \
&= x^T(A^T P + P A) x \
&= x^T Q x,
end{align}
$$
insert $A$ and $P$ and derive $Q$:
$$
begin{align}
Q = A^T P + P A &=
begin{bmatrix}
-1 & -3 & 0 & 0 \
3 & -1 & 0 & 0 \
0 & 0 & 0 & -3 \
0 & 0 & 3 & 0
end{bmatrix} frac{1}{2} I_4 + frac{1}{2} I_4
begin{bmatrix}
-1 & 3 & 0 & 0 \
-3 & -1 & 0 & 0 \
0 & 0 & 0 & 3 \
0 & 0 & -3 & 0
end{bmatrix} \ &=
begin{bmatrix}
-1 & 0 & 0 & 0 \
0 & -1 & 0 & 0 \
0 & 0 & 0 & 0 \
0 & 0 & 0 & 0 end{bmatrix} .
end{align}
$$
So your directional derivative is $dot{V}(x) = -x_1^2 - x_2^2$, which is negative semi-definite. Therefore, the system is Lyapunov stable (but not asymptotically Lyapunov stable).
1
In this case it might be even easier to just look at the eigenvalues, because they all have an algebraic multiplicity of one. Only if the algebraic multiplicity of an eigenvalue with zero real part is higher than one, you also need to check its geometric multiplicity.
– Kwin van der Veen
2 days ago
add a comment |
up vote
1
down vote
accepted
Write $I_n$ for a $n times n$ identity matrix.
Take $P = (1/2)I_4$ and $V(x) = x^T P x$, which is clearly positive definite. Now calculate the directional derivative:
$$
begin{align}
dot{V}(x) &= dot{x}^T P x + x^T P dot{x} \
&= x^T A^T P x + x^T P A x \
&= x^T(A^T P + P A) x \
&= x^T Q x,
end{align}
$$
insert $A$ and $P$ and derive $Q$:
$$
begin{align}
Q = A^T P + P A &=
begin{bmatrix}
-1 & -3 & 0 & 0 \
3 & -1 & 0 & 0 \
0 & 0 & 0 & -3 \
0 & 0 & 3 & 0
end{bmatrix} frac{1}{2} I_4 + frac{1}{2} I_4
begin{bmatrix}
-1 & 3 & 0 & 0 \
-3 & -1 & 0 & 0 \
0 & 0 & 0 & 3 \
0 & 0 & -3 & 0
end{bmatrix} \ &=
begin{bmatrix}
-1 & 0 & 0 & 0 \
0 & -1 & 0 & 0 \
0 & 0 & 0 & 0 \
0 & 0 & 0 & 0 end{bmatrix} .
end{align}
$$
So your directional derivative is $dot{V}(x) = -x_1^2 - x_2^2$, which is negative semi-definite. Therefore, the system is Lyapunov stable (but not asymptotically Lyapunov stable).
1
In this case it might be even easier to just look at the eigenvalues, because they all have an algebraic multiplicity of one. Only if the algebraic multiplicity of an eigenvalue with zero real part is higher than one, you also need to check its geometric multiplicity.
– Kwin van der Veen
2 days ago
add a comment |
up vote
1
down vote
accepted
up vote
1
down vote
accepted
Write $I_n$ for a $n times n$ identity matrix.
Take $P = (1/2)I_4$ and $V(x) = x^T P x$, which is clearly positive definite. Now calculate the directional derivative:
$$
begin{align}
dot{V}(x) &= dot{x}^T P x + x^T P dot{x} \
&= x^T A^T P x + x^T P A x \
&= x^T(A^T P + P A) x \
&= x^T Q x,
end{align}
$$
insert $A$ and $P$ and derive $Q$:
$$
begin{align}
Q = A^T P + P A &=
begin{bmatrix}
-1 & -3 & 0 & 0 \
3 & -1 & 0 & 0 \
0 & 0 & 0 & -3 \
0 & 0 & 3 & 0
end{bmatrix} frac{1}{2} I_4 + frac{1}{2} I_4
begin{bmatrix}
-1 & 3 & 0 & 0 \
-3 & -1 & 0 & 0 \
0 & 0 & 0 & 3 \
0 & 0 & -3 & 0
end{bmatrix} \ &=
begin{bmatrix}
-1 & 0 & 0 & 0 \
0 & -1 & 0 & 0 \
0 & 0 & 0 & 0 \
0 & 0 & 0 & 0 end{bmatrix} .
end{align}
$$
So your directional derivative is $dot{V}(x) = -x_1^2 - x_2^2$, which is negative semi-definite. Therefore, the system is Lyapunov stable (but not asymptotically Lyapunov stable).
Write $I_n$ for a $n times n$ identity matrix.
Take $P = (1/2)I_4$ and $V(x) = x^T P x$, which is clearly positive definite. Now calculate the directional derivative:
$$
begin{align}
dot{V}(x) &= dot{x}^T P x + x^T P dot{x} \
&= x^T A^T P x + x^T P A x \
&= x^T(A^T P + P A) x \
&= x^T Q x,
end{align}
$$
insert $A$ and $P$ and derive $Q$:
$$
begin{align}
Q = A^T P + P A &=
begin{bmatrix}
-1 & -3 & 0 & 0 \
3 & -1 & 0 & 0 \
0 & 0 & 0 & -3 \
0 & 0 & 3 & 0
end{bmatrix} frac{1}{2} I_4 + frac{1}{2} I_4
begin{bmatrix}
-1 & 3 & 0 & 0 \
-3 & -1 & 0 & 0 \
0 & 0 & 0 & 3 \
0 & 0 & -3 & 0
end{bmatrix} \ &=
begin{bmatrix}
-1 & 0 & 0 & 0 \
0 & -1 & 0 & 0 \
0 & 0 & 0 & 0 \
0 & 0 & 0 & 0 end{bmatrix} .
end{align}
$$
So your directional derivative is $dot{V}(x) = -x_1^2 - x_2^2$, which is negative semi-definite. Therefore, the system is Lyapunov stable (but not asymptotically Lyapunov stable).
answered 2 days ago
SampleTime
50739
50739
1
In this case it might be even easier to just look at the eigenvalues, because they all have an algebraic multiplicity of one. Only if the algebraic multiplicity of an eigenvalue with zero real part is higher than one, you also need to check its geometric multiplicity.
– Kwin van der Veen
2 days ago
add a comment |
1
In this case it might be even easier to just look at the eigenvalues, because they all have an algebraic multiplicity of one. Only if the algebraic multiplicity of an eigenvalue with zero real part is higher than one, you also need to check its geometric multiplicity.
– Kwin van der Veen
2 days ago
1
1
In this case it might be even easier to just look at the eigenvalues, because they all have an algebraic multiplicity of one. Only if the algebraic multiplicity of an eigenvalue with zero real part is higher than one, you also need to check its geometric multiplicity.
– Kwin van der Veen
2 days ago
In this case it might be even easier to just look at the eigenvalues, because they all have an algebraic multiplicity of one. Only if the algebraic multiplicity of an eigenvalue with zero real part is higher than one, you also need to check its geometric multiplicity.
– Kwin van der Veen
2 days ago
add a comment |
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1
Your $A$ matrix in not in Jordan form, but in its real Jordan form, so you have to look at the size of each full real Jordan block.
– Kwin van der Veen
2 days ago
I thought that a 4x4 real jordan form had an identity matrix in the upper right corner? like this:$begin{bmatrix}-1&3&1&0\-3&-1&0&1\0&0&0&3\0&0&-3&0 end{bmatrix}$
– user463102
2 days ago
1
Yes, only your example is not a real Jordan block, because then both 2x2 matrices on the diagonal need to be the same.
– Kwin van der Veen
2 days ago
Thank you. I did not know that a right upper 2x2 identity only appears if both 2x2 matrices on the diagonal are the same.
– user463102
yesterday