Nanopositioning systems driven by piezoelectric actuators are widely used in different fields. However, the hysteresis phenomenon is a major factor in reducing the positioning accuracy of piezoelectric actuators. This effect makes the task of accurate modeling and position control of piezoelectric actuators challenging. In this paper, the learning and generalization capabilities of the model are efficiently enhanced to describe and compensate for the rate-independent and rate-dependent hysteresis using a kernel-based learning method. The proposed model is inspired by the classical Preisach hysteresis model, in which a set of hysteresis operators is used to address the problem of mapping, and then least-squares support-vector machines (LSSVM) combined with a particle swarm optimization (PSO) algorithm are used for identification. Two control schemes are proposed for hysteresis compensation, and their performance is evaluated through real-time experiments on a nanopositioning platform. First, an inverse model-based feedforward controller is designed based on the LSSVM model, and then a combined feedback/feedforward control scheme is designed using a classical control strategy (PID) to further enhance the tracking performance. For performance evaluation, different datasets with a variety of hysteresis loops are used during the simulation and experimental procedures. The results show that the proposed method is successful in enhancing the generalization capabilities of LSSVM training and achieving the best tracking performance based on the combination of feedforward control and PID feedback control. The proposed control scheme outperformed the inverse Preisach model-based control scheme in terms of both positioning accuracy and execution time. The control scheme that uses the LSSVM based on nonlinear autoregressive exogenous (NARX) models has significantly less computational complexity compared to our control scheme but at the expense of accuracy.

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Nanopositioning technology is widely used in high-resolution applications. It often uses piezoelectric actuators due to their superior characteristics. However, piezoelectric actuators exhibit a hysteresis phenomenon that limits their positioning accuracy. To compensate for the hysteresis effect, developing an accurate hysteresis model of piezoelectric actuators is very important. This task is challenging, requiring some considerations of the multivalued mapping of hysteresis loops and the generalization capabilities of the model. This challenge can be dealt with by developing a machine learning-based model, whose inverse model can be used to efficiently design an accurate feedforward controller for hysteresis compensation. However, this approach depends on model accuracy and the type of data used to train the model. Thus, accurate prediction of the hysteresis behavior may not be guaranteed in the presence of disturbances. In this paper, a machine learning-based model is used to design a hysteresis compensator and then combined with a robust feedback controller to enhance the robustness of a nanopositioning control system. The proposed model is based on hysteresis operators, the least square support vector machine (LSSVM) method, and particle swarm optimization (PSO) algorithm. The inverse model is used to design the feedforward controller, and the RST controller is employed to develop feedback control. Our main contribution is the introduction of a hybrid controller capable of compensating for the hysteresis effect, and at the same time, eliminating remaining modeling errors and rejecting disturbances. The performance of the proposed approach is evaluated through MATLAB simulation, as well as through real-time experiments. The experimental results of our approach demonstrate superior tracking performance compared with the PID-LSSVM controller.

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