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?










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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

















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?










share|cite|improve this question




















  • 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















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?










share|cite|improve this question















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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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
















  • 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












1 Answer
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up vote
1
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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).






share|cite|improve this answer

















  • 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











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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).






share|cite|improve this answer

















  • 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















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).






share|cite|improve this answer

















  • 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













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).






share|cite|improve this answer












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).







share|cite|improve this answer












share|cite|improve this answer



share|cite|improve this answer










answered 2 days ago









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  • 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




    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


















 

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