An interative learning control scheme using the weighted least-squares method

Keigo Watanabe; Toshio Fukuda; Spyros G. Tzafestas

doi:10.1007/BF00303227

An interative learning control scheme using the weighted least-squares method

Keigo Watanabe, Toshio Fukuda, Spyros G. Tzafestas

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

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	https://doi.org/10.1007/BF00303227
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

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10.1007/BF00303227

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AU - Tzafestas, Spyros G.

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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 - Iterative learning control

KW - Kalman filter

KW - impulse response model

KW - robot manipulator

KW - sparse system structure

KW - weighted least-squares method

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An interative learning control scheme using the weighted least-squares method

Abstract

Keywords

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