Iterative learning control of a piezo-actuated positioning stage for micro/nano manipulation

The main objective of this investigation is to improve the tracking accuracy of a piezo-actuated positioning stage using an iterative learning control. First, to compensate for the tracking error of the piezo-actuated positioning stage that is caused by nonlinear hysteresis, the dynamics of the hysteresis is modeled using the Bouc-Wen model. The particle swarm optimization (PSO) is used to determine the parameters of the inverse-hysteresis model. Second, the design of an iterative learning control is presented. Based on the simulation, the appropriate value of the learning rate is determined. Finally, the efficacy of the approach is demonstrated to achieve high accuracy positioning via the real-time experiments. The experimental result of the piezo-actuated positioning stage is measured by the laser interferometer (HP-5529A). The experimental results show that the iterative learning control can compensate the hysteresis-caused tracking error and the positional accuracy of better than 100 nano-meter is readily achieved.