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oth α and β .
determination of Quantitative factor and proportion
factor: Choose { − 6,
−5,
−4,
−3,
−2,
−1,0,1,2,3,4,5,6
} as fuzzy
domains of e 、 α 、 β on the consider of Real-time
performance and complexity of control. Fuzzy domain {-
n,+n} is the discretization error of continuous change in
basic domain {-m,+m}. Where, quantitative factor
k = n / m , and the basic domain of location error e is [-
15 , +15]. Then quantitative factor of location error
k e = 6 /15 = 2 / 5 . For system controlled object β , whose
°
domain is [ − 45 ° , + 45 ], this proportion factor of location
error k β = 6 / 45 = 2/ 15 .
Selection of fuzzy linguistic variables and its fuzzy
subsets: According to system’s actual circumstance,
linguistic variables are dicided into seven values as
“positive big”(PB), “positive middle”(PM), “positive
small”(PS), “zero” (ZO), “negative small”(NS),
“negative middle”(NM) and “negative big”(NB). Fuzzy
subsets of linguistic variables’ domain are discribed with
subjection function μ ( x)
. For a domain
{ − 6,
−5,
−4,
−3,
−2,
−1,0,1,2,3,4,5,6
}, of fuzzy subsets on those
seven defined linguistic variables PB, PM, PS, ZO, NS,
NM and NB, the values of maximum subjection “1” is
set as follow.
μ PB( X ) = 1 X = 6
μ PM ( X ) = 1 X = 4
μ PS ( X ) = 1 X = 2
μ Z 0 ( X ) = 1
X = 0
μ NS ( X ) = 1 X = −2
μ NM ( X ) = 1
X = −4
μ NB ( X ) = 1 X = −6
Things are tended to be judged as the characteristic of
normal distribution, so the subjection function of fuzzy
set is dicided with normal distribution.
( )
⎟ ⎟ ⎞
⎜
⎜ ⎛
2
⎛ x − a ⎞
μ x = exp − ⎜ ⎟ (1)
⎝
⎝ b ⎠
⎠
Where, parameter a represents respectively 6, 4, 2, 0,
-2, -4, -6 for the fuzzy sets PB, PM, PS, ZO, NS, NM,
NB. Parameter b is chosen positive which is bigger than
0. The value of a has greater impact on the control. The
bigger value of b , the wider μ ( x)
diagram, the lower
resolution characteristics and error control sensitivity. On
the contrary, smaller value of b will be together with
thiner μ ( x)
diagram, better resolution characteristics and
error control sensitivity. Therefore, choose the fuzzy sets
with low resolution characteristics subjection function
while in big system error. With small system error or even
near 0, fuzzy sets of high resolution characteristics
subjection function is adopted, and b is 1 here.
For a integer domain N, these linguistic variables can
be represented with different ways, such as the method of
table, formula or graphics and so on. If the Gaussian
subjection function is chosen to transform fuzzy linguistic
variables value of input and output, the values of the
linguistic variables above are shown as in table 1.
According to pre-given inputs e i and α i , as well as its
quantitative factor, gain the quantitative level of e i and
TABLE I.
assignment table of linguistic variable value
α i in basic domain from n ie = ke
× ei
and niα = kα
× αi
.
From linguistic variables transforming table, identify the
fuzzy set of the element n i on biggest subjection
corresponding linguistic value. This fuzzy set is right the
fuzzification of input value.
V. EXPERIMENTAL RESULTS AND ANALYSIS
Figure 4. PC debugging interface
Code Warrior 3.0 of Metroworks company is used in
this system for exploiture and debugging. A debugging
host computer interface, which is shown in Fig.4, is
producted in order to get more basic data and signal
information for actual testing. The use of VC and
Microsoft MSComm control, makes communication
hardware more simple. After communication protocol is
set up, all data collected from single-chip microcomputer
can be sent to the host computer through the serial port
for observation and testing.
A. Image information acquisition analysis
With the intelligent vehicle debugging software of
SmartLab1.0, road image can be acquired from intelligent
vehicle camera. According to the quality of images,
whether the result of image acquisition system is good or
not can be judged. The image gotten from intelligent
vehicle’s carema is shown in Fig.5.
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