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Fig. 2. The flow-density fundamental diagram of highway trac
operators of the form finite a α d α
ds α where α
R
( s ). This is a non-commutative
d
s ds =[ ds s,s ds ]=1.
ring according to Weyl algebra, since
ds s
of constant parameters is said to be linearly
identifiable with respect to a finite set X =
The finite set θ =
{
θ 1 ,...,θ r }
of signals, the input
and output variables of a linear system for instance, if and only if it leads in the
operational domain:
{
x 1 ,...,x k }
= Q
(6)
where P and Q are respectively r
×
r and r
×
1 matrices, and the entries of P
and Q belong to span
= 0. Consider the
additive perturbation, that is y i = x i + ω i , then equation (6) become (7): (7):
= Q + Q
R ( s )[ ds ] (1 ,x 1 ,...,x k ). Moreover, det ( P )
(7)
where, Q is a 1 matrix with entries depending now on ω i .If ω is structured,
it means that ω i , i =1 , 2 ,...,k satisfies a linear differential equation with poly-
nomial coecients, and there would exist Δ
( s )[ ds ], such that by multiplying
both sides of equation (7) by Δ annihilates the structured perturbations:
R
ΔPθ = ΔQ
(8)
Multiplying both sides of equation (8) by suitable proper rational functions in
R
( s ) yields proper rational functions in all the coecients.
The unstructured perturbations are modeled as highly fluctuating noises,
which can be attenuated by invariant low-pass filters, such as F ( s )=1 /s ν ,
where ν
1 is a large enough real number.
4 The Parameter Identification Scheme
In the algebraic identification framework, a novel key parameters identification
method is proposed, and the non-linearity characteristic of trac dynamic is
locally transformed into a linearization model approximately. The identification
procedure consists of five steps.
Step 1: Convert the equilibrium speed-density function into a linearization form
so that the linear parameter identification procedure can be acquired. The log-
arithmic style of equation (4) is shown as follows:
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