State-transition matrix

In control theory, the state-transition matrix is a matrix whose product with the state vector $x$ at an initial time $t_{0}$ gives $x$ at a later time $t$. The state-transition matrix can be used to obtain the general solution of linear dynamical systems.

Linear systems solutions

The state-transition matrix is used to find the solution to a general state-space representation of a linear system in the following form

${\dot {\mathbf {x} }}(t)=\mathbf {A} (t)\mathbf {x} (t)+\mathbf {B} (t)\mathbf {u} (t),\;\mathbf {x} (t_{0})=\mathbf {x} _{0}$,

where $\mathbf {x} (t)$ are the states of the system, $\mathbf {u} (t)$ is the input signal, $\mathbf {A} (t)$ and $\mathbf {B} (t)$ are matrix functions, and $\mathbf {x} _{0}$ is the initial condition at $t_{0}$. Using the state-transition matrix $\mathbf {\Phi } (t,\tau )$, the solution is given by:

$\mathbf {x} (t)=\mathbf {\Phi } (t,t_{0})\mathbf {x} (t_{0})+\int _{t_{0}}^{t}\mathbf {\Phi } (t,\tau )\mathbf {B} (\tau )\mathbf {u} (\tau )d\tau$

The first term is known as the zero-input response and represents how the system's state would evolve in the absence of any input. The second term is known as the zero-state response and defines how the inputs impact the system.

Peano–Baker series

The most general transition matrix is given by the Peano–Baker series

$\mathbf {\Phi } (t,\tau )=\mathbf {I} +\int _{\tau }^{t}\mathbf {A} (\sigma _{1})\,d\sigma _{1}+\int _{\tau }^{t}\mathbf {A} (\sigma _{1})\int _{\tau }^{\sigma _{1}}\mathbf {A} (\sigma _{2})\,d\sigma _{2}\,d\sigma _{1}+\int _{\tau }^{t}\mathbf {A} (\sigma _{1})\int _{\tau }^{\sigma _{1}}\mathbf {A} (\sigma _{2})\int _{\tau }^{\sigma _{2}}\mathbf {A} (\sigma _{3})\,d\sigma _{3}\,d\sigma _{2}\,d\sigma _{1}+...$

where $\mathbf {I}$ is the identity matrix. This matrix converges uniformly and absolutely to a solution that exists and is unique.

Other properties

The state transition matrix $\mathbf {\Phi }$ satisfies the following relationships:

1. It is continuous and has continuous derivatives.

2, It is never singular; in fact $\mathbf {\Phi } ^{-1}(t,\tau )=\mathbf {\Phi } (\tau ,t)$ and $\mathbf {\Phi } ^{-1}(t,\tau )\mathbf {\Phi } (t,\tau )=I$, where $I$ is the identity matrix.

3. $\mathbf {\Phi } (t,t)=I$ for all $t$ .

4. $\mathbf {\Phi } (t_{2},t_{1})\mathbf {\Phi } (t_{1},t_{0})=\mathbf {\Phi } (t_{2},t_{0})$ for all $t_{0}\leq t_{1}\leq t_{2}$.

5. It satisfies the differential equation ${\frac {\partial \mathbf {\Phi } (t,t_{0})}{\partial t}}=\mathbf {A} (t)\mathbf {\Phi } (t,t_{0})$ with initial conditions $\mathbf {\Phi } (t_{0},t_{0})=I$.

6. The state-transition matrix $\mathbf {\Phi } (t,\tau )$, given by

$\mathbf {\Phi } (t,\tau )\equiv \mathbf {U} (t)\mathbf {U} ^{-1}(\tau )$

where the $n\times n$ matrix $\mathbf {U} (t)$ is the fundamental solution matrix that satisfies

${\dot {\mathbf {U} }}(t)=\mathbf {A} (t)\mathbf {U} (t)$ with initial condition $\mathbf {U} (t_{0})=I$.

7. Given the state $\mathbf {x} (\tau )$ at any time $\tau$, the state at any other time $t$ is given by the mapping

$\mathbf {x} (t)=\mathbf {\Phi } (t,\tau )\mathbf {x} (\tau )$

Estimation of the state-transition matrix

In the time-invariant case, we can define $\mathbf {\Phi }$, using the matrix exponential, as $\mathbf {\Phi } (t,t_{0})=e^{\mathbf {A} (t-t_{0})}$.

In the time-variant case, the state-transition matrix $\mathbf {\Phi } (t,t_{0})$ can be estimated from the solutions of the differential equation ${\dot {\mathbf {u} }}(t)=\mathbf {A} (t)\mathbf {u} (t)$ with initial conditions $\mathbf {u} (t_{0})$ given by $[1,\ 0,\ \ldots ,\ 0]^{T}$, $[0,\ 1,\ \ldots ,\ 0]^{T}$, ..., $[0,\ 0,\ \ldots ,\ 1]^{T}$. The corresponding solutions provide the $n$ columns of matrix $\mathbf {\Phi } (t,t_{0})$. Now, from property 4, $\mathbf {\Phi } (t,\tau )=\mathbf {\Phi } (t,t_{0})\mathbf {\Phi } (\tau ,t_{0})^{-1}$ for all $t_{0}\leq \tau \leq t$. The state-transition matrix must be determined before analysis on the time-varying solution can continue.

See also

• Magnus expansion
• Liouville's formula

Further reading

• Baake, M.; Schlaegel, U. (2011). "The Peano Baker Series". Proceedings of the Steklov Institute of Mathematics. 275: 155–159. doi:10.1134/S0081543811080098. S2CID 119133539.
• Brogan, W.L. (1991). Modern Control Theory. Prentice Hall. ISBN 0-13-589763-7.