Accelerated Convergence Interleaving Iterative Learning Control and Inverse Dynamics Identification

This work aims to quickly identify an FIR inverse dynamical model for linear time-invariant (LTI) systems. Various applications are enabled using the constructed inverse filter, as illustrated by an inversion-based iterative learning control (ILC) algorithm. With the help of interleaving inversion-b...

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Bibliographic Details
Published inIEEE transactions on control systems technology Vol. 30; no. 1; pp. 45 - 56
Main Authors Chen, Cheng-Wei, Tsao, Tsu-Chin
Format Journal Article
LanguageEnglish
Published New York IEEE 01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Computational modeling
Convergence
Dynamic models
Feedforward systems
Finite impulse response filters
Heuristic algorithms
Inverse dynamics
inverse problems
iterative learning control (ILC)
Iterative methods
Machine learning
motion control
Numerical models
Robustness (mathematics)
Semiconductor device modeling
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Summary:This work aims to quickly identify an FIR inverse dynamical model for linear time-invariant (LTI) systems. Various applications are enabled using the constructed inverse filter, as illustrated by an inversion-based iterative learning control (ILC) algorithm. With the help of interleaving inversion-based ILC and ILC-based inverse dynamics identification, accelerated convergence is obtained. The proposed method removes the numerical instability issues in the calculation of an inverse model. Hence, it is shown more robust against measurement noises. Both simulation comparison and experimental results demonstrate the efficacy and advantages of the proposed strategy.
Bibliography:ObjectType-Article-1
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ISSN:1063-6536
1558-0865
DOI:10.1109/TCST.2021.3053561