Sampled-Data Adaptive ESO Based Composite Control for Nano-Positioning Systems

This work studies a novel sampled-data adaptive extended state observer (ESO) based composite control approach for the digital nano-positioning systems with sampling restrictions. In the proposed control architecture, a sampled-data ESO with an output predictor is designed to estimate states and disturbances within a continuous period of time, where the observer gains are adaptively updated. To deal with the inter-sample disturbances, a multirate disturbance compensator is then worked out by upsampling, with which a discrete composite feedback controller with linear and nonlinear parts is employed aiming at improving the transient performance of positioning. Moreover, the estimation convergence and the stability are analyzed by means of Lyapunov methods. The proposed control method is then applied to the simulations and experiments in a piezoelectric nano-positioning system so as to validate its effectiveness.