Optimizing Learning Convergence Speed and Converged Error for Precision Motion Control
This brief paper considers iterative learning control (ILC) for precision motion control (PMC) applications. This work develops a methodology to design a low pass filter, called the Q-filter, that is used to limit the bandwidth of the ILC to prevent the propagation of high frequencies in the learning. A time-varying bandwidth Q-filter is considered because PMC reference trajectories can exhibit rapid changes in acceleration that may require high bandwidth for short periods of time. Time-frequency analysis of the initial error signal is used to generate a shape function for the bandwidth profile. Key parameters of the bandwidth profile are numerically optimized to obtain the best tradeoff in converged error and convergence speed. Simulation and experimental results for a permanent-magnet linear motor are included. Results show that the optimal time-varying Q-filter bandwidth provides faster convergence to lower error than the optimal time-invariant bandwidth.
D. A. Bristow et al., "Optimizing Learning Convergence Speed and Converged Error for Precision Motion Control," Journal of Dynamic Systems, Measurement, and Control, American Society of Mechanical Engineers (ASME), Sep 2008.
The definitive version is available at https://doi.org/10.1115/1.2936844
Mechanical and Aerospace Engineering
Keywords and Phrases
Adaptive Control; Iterative Methods; Learning Systems; Low-Pass Filters; Motion Control; Precision Engineering; Servomechanisms; Time-Frequency Analysis; Time-Varying Filters
Article - Journal
© 2008 American Society of Mechanical Engineers (ASME), All rights reserved.