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 |
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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 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
- Electrical and Electronic Engineering
- Artificial Intelligence