Abstract
An iterative learning control scheme is described for linear discrete-time systems. A weighted least-squares criterion of learning error is optimized to obtain a unique control gain for a case when the number of sampling is relatively small. It is then shown that algorithmic convergence can be readily guaranteed, because the present learning rule consists of a steady-state Kalman filter. By paying attention to the sparse system structure for the system's impulse response model, we further derive a suboptimal iterative learning control for a practical case when the number of sampling is large.
| Original language | English |
|---|---|
| Pages (from-to) | 267-284 |
| Number of pages | 18 |
| Journal | Journal of Intelligent and Robotic Systems: Theory and Applications |
| Volume | 4 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - Sep 1991 |
| Externally published | Yes |
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Keywords
- impulse response model
- Iterative learning control
- Kalman filter
- robot manipulator
- sparse system structure
- weighted least-squares method
ASJC Scopus subject areas
- Control and Systems Engineering
- Artificial Intelligence
Cite this
An interative learning control scheme using the weighted least-squares method. / Watanabe, Keigo; Fukuda, Toshio; Tzafestas, Spyros G.
In: Journal of Intelligent and Robotic Systems: Theory and Applications, Vol. 4, No. 3, 09.1991, p. 267-284.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - An interative learning control scheme using the weighted least-squares method
AU - Watanabe, Keigo
AU - Fukuda, Toshio
AU - Tzafestas, Spyros G.
PY - 1991/9
Y1 - 1991/9
N2 - An iterative learning control scheme is described for linear discrete-time systems. A weighted least-squares criterion of learning error is optimized to obtain a unique control gain for a case when the number of sampling is relatively small. It is then shown that algorithmic convergence can be readily guaranteed, because the present learning rule consists of a steady-state Kalman filter. By paying attention to the sparse system structure for the system's impulse response model, we further derive a suboptimal iterative learning control for a practical case when the number of sampling is large.
AB - An iterative learning control scheme is described for linear discrete-time systems. A weighted least-squares criterion of learning error is optimized to obtain a unique control gain for a case when the number of sampling is relatively small. It is then shown that algorithmic convergence can be readily guaranteed, because the present learning rule consists of a steady-state Kalman filter. By paying attention to the sparse system structure for the system's impulse response model, we further derive a suboptimal iterative learning control for a practical case when the number of sampling is large.
KW - impulse response model
KW - Iterative learning control
KW - Kalman filter
KW - robot manipulator
KW - sparse system structure
KW - weighted least-squares method
UR - http://www.scopus.com/inward/record.url?scp=34249916826&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34249916826&partnerID=8YFLogxK
U2 - 10.1007/BF00303227
DO - 10.1007/BF00303227
M3 - Article
AN - SCOPUS:34249916826
VL - 4
SP - 267
EP - 284
JO - Journal of Intelligent and Robotic Systems: Theory and Applications
JF - Journal of Intelligent and Robotic Systems: Theory and Applications
SN - 0921-0296
IS - 3
ER -