Accelerated Convergence Interleaving Iterative Learning Control and Inverse Dynamics Identification
Journal
IEEE Transactions on Control Systems Technology
Journal Volume
30
Journal Issue
1
Pages
45-56
Date Issued
2022
Author(s)
Chen C.-W
Abstract
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. ? 1993-2012 IEEE.
Subjects
Feedforward systems
Finite impulse response filters
Inverse problems
Iterative learning control (ILC)
Motion control
Iterative methods
Learning algorithms
Numerical methods
Accelerated convergence
Inverse dynamics
Inverse modeling
Iterative learning control
Linear time-invariant system
Measurement Noise
Numerical instability
Simulation comparison
Two term control systems
Type
journal article