An Adaptive Fuzzy Predictive Controller with Hysteresis Compensation for Piezoelectric Actuators

Piezoelectric actuators (PEAs) are the pivotal components of many nanopositioning systems because of their superiorities in bandwidth, mechanical force, and precision. Unfortunately, the intrinsic nonlinear property, hysteresis, makes it difficult to achieve the precise control of PEAs. Considering this drawback, diversified feedback control approaches have been studied in the literature. Inspired by the idea that the involvement of feedforward terms can upgrade the tracking performance, our previous conference paper proposed a novel feedforward–feedback control approach (model predictive control with hysteresis compensation). Following the previous work, an adaptive fuzzy predictive controller with hysteresis compensation is further studied in this paper. The major improvement of the proposed method is the employment of adaptive fuzzy model, by which the dynamic model of PEAs is able to adjust in real time, resulting in a better control performance. To validate the effectiveness of the proposed method, extensive experiments are conducted on a Physik Instrumente P-753.1CD piezoelectric nanopositioning stage. Comparisons with several existing control approaches are carried out, and the root mean square tracking error of the proposed method is reduced to 30% of that under the previously proposed neural network model–based predictive control, when tracking 100 Hz sinusoidal reference.