Modeling and Control of a Linear Piezoelectric Actuator

Abstract

To improve the output displacement of piezoelectric actuators, a linear piezoelectric actuator based on a multistage amplifying mechanism with a small volume, large thrust, high resolution, high precision, and fast response speed is proposed. However, inherent nonlinear characteristics, such as hysteresis and creep, significantly affect the output accuracy of piezoelectric actuators and may cause system instability. Therefore, a complex nonlinear hysteresis mathematical model with a high degree of fit was established. A Play operator was introduced into the backpropagation neural network, and a genetic algorithm (GA) was used to reduce the probability of the fitting of the neural network model falling into a local minimum. Moreover, simulation and experimental test platforms were constructed. The results showed that the maximum displacement of the actuator was 558.3 μm under a driving voltage of 150 V and a driving frequency of 1 Hz. The complex GA-BP neural network model of the piezoelectric actuator not only exhibited high modeling accuracy but also solved the problems of strong randomness and slow convergence. Compared with other control algorithms, the GA-BP fuzzy PID control exhibited higher control precision.

Adaptive Backstepping Time Delay Control for Precision Positioning Stage with Unknown Hysteresis

“…However, the hysteresis present in smart materials often severely degrades the performance of mechatronic systems and can even lead to system instability [5]. Moreover, compared with traditional nonlinearities, the multivalued mapping [6], memory effects [5], and rate-dependency [7] make its modeling and control exceptionally challenging.Therefore, over the past thirty years, the problem of modeling hysteresis characteristics and their compensation control has attracted the attention of many scholars [8]. In the aspect of hysteresis modeling, there are currently three main methods.…”