Solution to the linear parameterization of the given system:
$\ y = \frac{b}{s+a}e^{-sτ}u = \frac{b}{s+a}e^{-sτ}u \ + \ \frac{b}{s+a}u \ - \ \frac{b}{s+a}u = \frac{b}{s+a}u \ + \ \frac{b}{s+a}(e^{-s\tau} \ - \ 1)u $
$$ sy = -ay+bu+b(e^{-s\tau}-1)u $$
We filter both sides of the equation with a stable first order filter
$$ Λ(s) = s + l_1, \ l_1 >0 $$
and the final parametric model is now:
$\ \frac{s}{Λ(s)}y = -a\frac{1}{Λ(s)}y+b\frac{1}{Λ(s)}u+ \frac{b}{Λ(s)}(e^{-s\tau}-1)u$
And the last equation can be written in the form:
$$ z = \theta^{*T}\phi + η $$
where $\ z = \frac{s}{Λ(s)}y, \ \theta^* = [a \ b], \ \phi = [-\frac{1}{Λ(s)}y \ \ \frac{1}{Λ(s)}u]^T $ and $\ η = \frac{b}{Λ(s)}(e^{-sτ}-1)u $.
It is obvious that for $\ \tau = 0 \to η=0. $
The new input vector of the parametric system $\ z $, can be derived directly from the input of the initial system $\ y. $
EDIT: Slightly different approach
$$ \frac{s}{Λ(s)}y = -a\frac{1}{Λ(s)}y+b\frac{1}{Λ(s)}u+ \frac{b}{Λ(s)}(e^{-s\tau}-1)u $$
From this point, we can come up with a linear parametric model which contains the initial input vector $\ y $ following the procedure below:
$$ Λ(s) = s + l_1 \Rightarrow s = Λ(s) - l_1, \ l_1>0$$
$$ \frac{Λ(s) - l_1}{Λ(s)}y = -a\frac{1}{Λ(s)}y+b\frac{1}{Λ(s)}u+ \frac{b}{Λ(s)}(e^{-s\tau}-1)u \Rightarrow $$
$$ \frac{Λ(s)}{Λ(s)}y - \frac{l_1}{Λ(s)}y = -a\frac{1}{Λ(s)}y+b\frac{1}{Λ(s)}u+ \frac{b}{Λ(s)}(e^{-s\tau}-1)u \Rightarrow $$
$$ y = -(a-l_1)\frac{1}{Λ(s)}y +b\frac{1}{Λ(s)}u+ \frac{b}{Λ(s)}(e^{-s\tau}-1)u $$
and the last equation can be written in the form:
$$ y = \theta^{*T}\phi + η $$
where $\ \theta^* = [(a-l_1) \ \ b], \ \phi = [-\frac{1}{Λ(s)}y \ \ \frac{1}{Λ(s)}u]^T $ and $\ η = \frac{b}{Λ(s)}(e^{-sτ}-1)u $.
It is obvious that for $\ \tau = 0 \Rightarrow η=0. $