化材系專題演講集錦 – 研究所
講題
講者
Use of Multiple Integration and Laguerre Models for System
Identification: Methods Concerning Practical Operating Conditions
黃宇璋
服務
單位
高雄應用科技
職稱
大學化材系
時間
99 年 9 月 24 日(星期五)14 時 30 分至 16 時 20 分
地點
化工館 1F 曉東講堂
主持人 化材系 何宗漢主任
演講內容電子檔：有
參加人數：108 人
參加對象：本系師生
助理教授
演
講
內
容
摘
要
System identification finding process and disturbance models based on input-output testing is often
faced with practical operating conditions as follows:
1.unsteady and unknown initial states
2.load disturbances of unknown dynamics and unpredicted nature
3.stochastic disturbances
4.unknown model structure (order and delay) and parameters
5.constraints on the input signal to a test experiment
6.continuous-time or discrete-time
Identification Method for Continuous-Time SISO Systems
．Use of multiple integration to avoid time derivatives of the input-output signals
．A sequential least-squares method that identifies a parametric model using a two-segment test
signal (first complicated and then simple) in face of the practical difficulties
．A convenient technique to determine the model structure based on the same test data
．The method is robust with respect to unsteady initial states, unknown load disturbances, noise, and
model structure mismatch
SISO Identification Model
Y ( z )  GP ( z )U D ( z )  GI ( z )  GL1 ( z ) S ( z )
Y(z), UD(z): z–transforms of
the output y(k) and delayed input
uD(k) = u(k - d)
GI(z): initial states ; GV(z): stochastic disturbance
GL1(z), GL2(z), dL: first and second load disturbances and load entering time
 GL2 ( z ) S ( z ) z  L  GV ( z ) E ( z )
Y  z 
BP  z 
zB  z 
zBL1  z 
UD  z   I

AP  z 
AP  z   z  1 AP  z  AL1  z 

z - L 1BL2  z 
 z  1 AP  z  AL2  z 
AL1(z), AL2(z): distinct load dynamics
AL (z): monic polynomial of degree nL, the least
common multiple of AL1(z), AL2(z)
AP(z): denominator polynomial for process of order nP
Conclusion
three effective methods to deal with system identification based on plant tests under practical operating
conditions have be developed :
．The first method using multiple integration and a sequential algorithm can identify a
continuous-time SISO process from a relatively simple test experiment
．The second method based on a single Laguerre model with an adjustable time-scaling factor can
identify a discrete-time MIMO process if the process order is not too high
．The third method based on double Laguerre models with different time-scaling factors can identify
a good reduced-order model for a discrete-time MIMO process