Extreme precise motion tracking of piezoelectric positioning stage using sampled-data iterative learning control

Positioning stages using piezoelectric stack actuators have been widely used in industrial applications. In this work we explore practical control algorithms that can achieve extreme precision motion tracking. The extreme precision is defined as the acquisition of tracking accuracy up to the hardware limit of a control system, for instance, the quantization limit of analog-digital converters (ADC). Sampled-data feedback control algorithms are unable to achieve such extreme precision tracking due to the inherent sampling delay that causes phase lag and limited control gain. In this paper we apply an iterative learning control (ILC) approach that can achieve the extreme precision for motion tracking tasks that repeat. ILC is essentially a feedforward control approach that fully utilizes the past control information, hence is able to overcome the limit of feedback algorithms. The sampled-data ILC is implemented on a piezoelectric positioning stage, in which the ADC device has a limited quantization of 49 nanometers. With only a few iterations of learning, the achieved tracking accuracy is 49 nanometers. Comparing with well tuned feedback control algorithm, ILC can further reduce the tracking error by 20 times.