Time-invariant system

In control theory, a time-invariant (TI) system has a time-dependent system function that is not a direct function of time. Such systems are regarded as a class of systems in the field of system analysis. The time-dependent system function is a function of the time-dependent input function. If this function depends only indirectly on the time-domain (via the input function, for example), then that is a system that would be considered time-invariant. Conversely, any direct dependence on the time-domain of the system function could be considered as a "time-varying system".

Block diagram illustrating the time invariance for a deterministic continuous-time single-input single-output system. The system is time-invariant if and only if y₂(t) = y₁(t – t₀) for all time t, for all real constant t₀ and for all input x₁(t).^[1][2][3] Click image to expand it.

Mathematically speaking, "time-invariance" of a system is the following property:^{[4]: p. 50}

Given a system with a time-dependent output function ⁠${\displaystyle y(t)}$⁠, and a time-dependent input function ⁠${\displaystyle x(t)}$⁠, the system will be considered time-invariant if a time-delay on the input ⁠${\displaystyle x(t+\delta )}$⁠ directly equates to a time-delay of the output ⁠${\displaystyle y(t+\delta )}$⁠ function. For example, if time ⁠${\displaystyle t}$⁠ is "elapsed time", then "time-invariance" implies that the relationship between the input function ⁠${\displaystyle x(t)}$⁠ and the output function ⁠${\displaystyle y(t)}$⁠ is constant with respect to time ⁠${\displaystyle t:}$⁠

${\displaystyle y(t)=f(x(t),t)=f(x(t)).}$

In the language of signal processing, this property can be satisfied if the transfer function of the system is not a direct function of time except as expressed by the input and output.

In the context of a system schematic, this property can also be stated as follows, as shown in the figure to the right:

If a system is time-invariant then the system block commutes with an arbitrary delay.

If a time-invariant system is also linear, it is the subject of linear time-invariant theory (linear time-invariant) with direct applications in NMR spectroscopy, seismology, circuits, signal processing, control theory, and other technical areas. Nonlinear time-invariant systems lack a comprehensive, governing theory. Discrete time-invariant systems are known as shift-invariant systems. Systems which lack the time-invariant property are studied as time-variant systems.

Simple example

To demonstrate how to determine if a system is time-invariant, consider the two systems:

• System A: ${\displaystyle y(t)=tx(t)}$
• System B: ${\displaystyle y(t)=10x(t)}$

Since the System Function ${\displaystyle y(t)}$ for system A explicitly depends on t outside of ${\displaystyle x(t)}$, it is not time-invariant because the time-dependence is not explicitly a function of the input function.

In contrast, system B's time-dependence is only a function of the time-varying input ${\displaystyle x(t)}$. This makes system B time-invariant.

The Formal Example below shows in more detail that while System B is a Shift-Invariant System as a function of time, t, System A is not.

Formal example

A more formal proof of why systems A and B above differ is now presented. To perform this proof, the second definition will be used.

System A: Start with a delay of the input ${\displaystyle x_{d}(t)=x(t+\delta )}$

${\displaystyle y(t)=tx(t)}$

${\displaystyle y_{1}(t)=tx_{d}(t)=tx(t+\delta )}$

Now delay the output by ${\displaystyle \delta }$

${\displaystyle y(t)=tx(t)}$

${\displaystyle y_{2}(t)=y(t+\delta )=(t+\delta )x(t+\delta )}$

Clearly ${\displaystyle y_{1}(t)\neq y_{2}(t)}$, therefore the system is not time-invariant.

System B: Start with a delay of the input ${\displaystyle x_{d}(t)=x(t+\delta )}$

${\displaystyle y(t)=10x(t)}$

${\displaystyle y_{1}(t)=10x_{d}(t)=10x(t+\delta )}$

Now delay the output by ${\displaystyle \delta }$

${\displaystyle y(t)=10x(t)}$

${\displaystyle y_{2}(t)=y(t+\delta )=10x(t+\delta )}$

Clearly ${\displaystyle y_{1}(t)=y_{2}(t)}$, therefore the system is time-invariant.

More generally, the relationship between the input and output is

${\displaystyle y(t)=f(x(t),t),}$

and its variation with time is

${\displaystyle {\frac {\mathrm {d} y}{\mathrm {d} t}}={\frac {\partial f}{\partial t}}+{\frac {\partial f}{\partial x}}{\frac {\mathrm {d} x}{\mathrm {d} t}}.}$

For time-invariant systems, the system properties remain constant with time,

${\displaystyle {\frac {\partial f}{\partial t}}=0.}$

Applied to Systems A and B above:

${\displaystyle f_{A}=tx(t)\qquad \implies \qquad {\frac {\partial f_{A}}{\partial t}}=x(t)\neq 0}$ in general, so it is not time-invariant,

${\displaystyle f_{B}=10x(t)\qquad \implies \qquad {\frac {\partial f_{B}}{\partial t}}=0}$ so it is time-invariant.

Abstract example

We can denote the shift operator by ${\displaystyle \mathbb {T} _{r}}$ where ${\displaystyle r}$ is the amount by which a vector's index set should be shifted. For example, the "advance-by-1" system

${\displaystyle x(t+1)=\delta (t+1)*x(t)}$

can be represented in this abstract notation by

${\displaystyle {\tilde {x}}_{1}=\mathbb {T} _{1}{\tilde {x}}}$

where ${\displaystyle {\tilde {x}}}$ is a function given by

${\displaystyle {\tilde {x}}=x(t)\forall t\in \mathbb {R} }$

with the system yielding the shifted output

${\displaystyle {\tilde {x}}_{1}=x(t+1)\forall t\in \mathbb {R} }$

So ${\displaystyle \mathbb {T} _{1}}$ is an operator that advances the input vector by 1.

Suppose we represent a system by an operator ${\displaystyle \mathbb {H} }$. This system is time-invariant if it commutes with the shift operator, i.e.,

${\displaystyle \mathbb {T} _{r}\mathbb {H} =\mathbb {H} \mathbb {T} _{r}\forall r}$

If our system equation is given by

${\displaystyle {\tilde {y}}=\mathbb {H} {\tilde {x}}}$

then it is time-invariant if we can apply the system operator ${\displaystyle \mathbb {H} }$ on ${\displaystyle {\tilde {x}}}$ followed by the shift operator ${\displaystyle \mathbb {T} _{r}}$, or we can apply the shift operator ${\displaystyle \mathbb {T} _{r}}$ followed by the system operator ${\displaystyle \mathbb {H} }$, with the two computations yielding equivalent results.

Applying the system operator first gives

${\displaystyle \mathbb {T} _{r}\mathbb {H} {\tilde {x}}=\mathbb {T} _{r}{\tilde {y}}={\tilde {y}}_{r}}$

Applying the shift operator first gives

${\displaystyle \mathbb {H} \mathbb {T} _{r}{\tilde {x}}=\mathbb {H} {\tilde {x}}_{r}}$

If the system is time-invariant, then

${\displaystyle \mathbb {H} {\tilde {x}}_{r}={\tilde {y}}_{r}}$

See also

• Finite impulse response
• Sheffer sequence
• State space (controls)
• Signal-flow graph
• LTI system theory
• Autonomous system (mathematics)

References

1. Bessai, Horst J. (2005). MIMO Signals and Systems. Springer. p. 28. ISBN 0-387-23488-8.
2. Sundararajan, D. (2008). A Practical Approach to Signals and Systems. Wiley. p. 81. ISBN 978-0-470-82353-8.
3. Roberts, Michael J. (2018). Signals and Systems: Analysis Using Transform Methods and MATLAB® (3 ed.). McGraw-Hill. p. 132. ISBN 978-0-07-802812-0.
4. Oppenheim, Alan; Willsky, Alan (1997). Signals and Systems (second ed.). Prentice Hall.