Adaptive Iterative Learning Control for High Precision Motion Systems

Abstract

Abstract-Iterative learning control (ILC) is a very effectivetechnique to reduce systematic errors that occur in systems that repetitively perform the same motion or operation. However, several characteristics have prevented standard ILC from being widely used for high precision motion systems. Most importantly, the learned feedforward signal depends on the motion profile (setpoint trajectory) and if this is altered, the learning process has to be repeated. Secondly, ILC amplifies non-repetitive disturbances and noise. Finally, its performance may be limited due to position-dependent dynamics. This paper presents the design and implementation of a time-frequency adaptive ILC that is applicable for motion systems which executes the same motion or operation. It employs the same control system block diagram as standard ILC, but instead of a fixed robustness filter it uses a time-varying filter. By using the results of the time-frequency adaptive ILC, i.e., the shape of the learned feedforward signal, a "piecewise ILC" is proposed that leads to the design of a single learned feedforward signal suitable for different setpoints. The results are experimentally shown to work for a high precision motion system.

A unified data-driven design framework of optimality-based generalized iterative learning control

“…The first control scenario of tracking an entire reference trajectory is most common in practical applications (Uchiyama, 1978;Arimoto et al, 1984;Xu and Tan, 2003;Bristow et al, 2006;Ahn et al, 2007;Saab, 1994;Park et al, 1999;Sun and Wang, 2002;Tayebi, 2004;Xu and Xu, 2004;Rotariu et al, 2008;Chi et al, 2008;Hwang et al, 1991;Amann et al, 1996;Lee et al, 2000;Gunnarsson and Norrlof, 2001;Sun and Alleyne, 2014) 1994; Park et al, 1999;Sun and Wang, 2002), Lyapunov function based adaptive ILC (AILC) (Tayebi, 2004;Xu and Xu, 2004;Rotariu et al, 2008;Chi et al, 2008), and optimization based optimal ILC (OILC) (Hwang et al, 1991;Amann et al, 1996;Lee et al, 2000;Gunnarsson and Norrlof, 2001;Sun and Alleyne, 2014). The optimal ILC is most popular in practice because it can reject the undesirable large transient behavior that exists in PID-ILCs and AILCs, and has a monotonic convergence performance along the iteration direction.…”

Using iterative learning control with basis functions to compensate medium deformation in a wide-format inkjet printer

“…The use of such a signal library is restricting in the sense that the tasks are required to consist of standardized building blocks that must be learned a priori. The use of a time-varying robustness filter [7,30] introduces extrapolation capabilities for specific filter structures [30], but only for a restricted class of reference variations. In [13] an initial input selection for ILC is proposed.…”

Second-Order Iterative Learning Control for Scaled Setpoints

“…In [7] and [8], time-frequency adaptive ILC is proposed, where different setpoints are generated using a constant velocity phase with variable length. More general trajectories are considered in [9] in which a finite impulse response mapping strategy is proposed based on converged learning forces obtained with learning control at a specific acceleration set-point profile.…”