Transformation matrix of linear n-dimensional state-space system

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SUMMARY

The discussion focuses on the transformation matrix P for a linear n-dimensional state-space system defined by the equation {\dot{\vec{{x}}} = {\bf{A}}{\vec{{x}}} + {\bf{B}}{\vec{{u}}}. The transformation matrix is constructed using the controllability matrix M_c and a secondary matrix M_2 derived from the characteristic equation |I\lambda -A|. The relationship is established as P^{-1} = M_c M_2, leading to the canonical form {\overline{\bf{A}}} = {\bf{PAP}}^{-1}. The user seeks clarification on the intuition behind this transformation process, particularly the role of M_2.

PREREQUISITES
  • Understanding of linear algebra concepts, particularly matrices and transformations.
  • Familiarity with state-space representation in control systems.
  • Knowledge of controllability and the controllability matrix M_c.
  • Ability to derive and interpret characteristic equations of matrices.
NEXT STEPS
  • Study the derivation and properties of the controllability matrix M_c in detail.
  • Explore the canonical forms of state-space systems and their significance in control theory.
  • Learn about the implications of full rank in the context of controllability.
  • Investigate the role of characteristic equations in system stability and behavior.
USEFUL FOR

Control system engineers, linear algebra students, and anyone involved in the analysis and design of state-space systems will benefit from this discussion.

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Hi all,

I have a linear algebra question relating actually to control systems (applied differential equations)

for the linear system

<br /> <br /> {\dot{\vec{{x}}} = {\bf{A}}{\vec{{x}}} + {\bf{B}}}{\vec{{u}}}\\<br /> \\<br /> <br /> A \in \mathbb{R}^{ nxn }\\<br /> B \in \mathbb{R}^{ nx1 }\\<br />

In class, we formed a transformation matrix P using the controllability matrix M_c as a basis (assuming it is full rank).
<br /> <br /> M_c = [ {\bf{B \;AB \;A^2B\;...\;A^{n-1}B}}]<br />

and there is a second matrix with a less established name. Given that the characteristic equation of the system is |I\lambda -A| = \lambda^n + \alpha_1 \lambda^{n-1} +... + \alpha_{n-1}\lambda + \alpha_n= 0, we then construct a second matrix, call it M_2, which is given below.

<br /> {\bf{M}}_2 = <br /> \begin{bmatrix}<br /> \alpha_{n-1} &amp; \alpha_{n-2} &amp; \cdots &amp; \alpha_1 &amp; 1 \\<br /> \alpha_{n-2} &amp; \cdots &amp; \alpha_1 &amp; 1 &amp; 0 \\<br /> \vdots &amp; \alpha_1 &amp; 1 &amp; 0 &amp; 0\\<br /> \alpha_1 &amp; 1 &amp; 0 &amp; \cdots &amp; 0\\<br /> 1 &amp; 0 &amp; 0&amp; \cdots &amp; 0 \\<br /> \end{bmatrix}<br /> <br />

then the transformation matrix is then given by

<br /> <br /> P^{-1} = M_c M_2<br /> <br />and then applying the transformation always gives.. and this is what I don't understand...

<br /> {\overline{\bf{A}}} = {\bf{PAP}}^{-1} = <br /> <br /> \begin{bmatrix}<br /> 0 &amp; 1 &amp; 0 &amp; \cdots &amp; 0 \\<br /> 0 &amp; 0 &amp; 1 &amp; \cdots &amp; \vdots \\<br /> \vdots &amp; \vdots &amp; 0 &amp; 1 &amp; 0\\<br /> 0 &amp; 0 &amp; \cdots &amp;0&amp; 1\\<br /> -\alpha_{1} &amp; -\alpha_{2} &amp; \cdots &amp; -\alpha_{n-1}&amp; -\alpha_{n}\\<br /> \end{bmatrix}<br /> <br />

Now I'm just looking for intuition is to why this is true. I know that this only works if the controllability matrix is full rank, which can the be used as a basis for the new transformation, but I don't get how exactly the M_2 matrix is using it to transform into the canonical form... Can someone explain this to me? thanks...All Right! I think I'm done editin LATEX ...
 
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Would I do better posting this somewhere else in PF?
 

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