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 & Robotic Systems |
| Volume | 4 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - Sep 1 1991 |
| Externally published | Yes |
Keywords
- Iterative learning control
- Kalman filter
- impulse response model
- robot manipulator
- sparse system structure
- weighted least-squares method
ASJC Scopus subject areas
- Software
- Control and Systems Engineering
- Mechanical Engineering
- Industrial and Manufacturing Engineering
- Artificial Intelligence
- Electrical and Electronic Engineering
Fingerprint Dive into the research topics of 'An interative learning control scheme using the weighted least-squares method'. Together they form a unique fingerprint.
Cite this
Watanabe, K., Fukuda, T., & Tzafestas, S. G. (1991). An interative learning control scheme using the weighted least-squares method. Journal of Intelligent & Robotic Systems, 4(3), 267-284. https://doi.org/10.1007/BF00303227