Proportional–Integral–Derivative-Based Learning Control for High-Accuracy Repetitive Positioning of Frictional Motion Systems

Classical proportional-integral-derivative (PID) control is exploited widely in industrial motion systems with dry friction motivated by the intuitive and easy-to-use design tuning tools available. However, classical PID suffers from severe performance limitations. In particular, friction-induced limit cycling (i.e., hunting) observed when integral employed on frictional that suffer Stribeck effect, thereby compromising setpoint stability. addition, resulting time-domain behavior, such as rise time, overshoot, settling positioning accuracy, highly depends particular characteristic, which typically unknown or uncertain. On other hand, omitting can lead to constant nonzero errors stick). To achieve superior for a repetitive setting, we propose PID-based feedback controller time-varying integrator gain design. ensure optimal data-based sampled-data extremum-seeking architecture obtain The proposed approach does not rely knowledge characteristic. Finally, effectiveness of evidenced experimentally application an nanopositioning stage setup high-end electron microscope.