Generalized regression neural network modeling based on inverse Duhem operator and adaptive sliding mode control for hysteresis in piezoelectric actuators

A dynamic generalized regression neural network model based on inverse Duhem operator is proposed to characterize the rate-dependent hysteresis in piezoelectric actuators. As hysteresis is multi-valued mapping, and traditional neural network can only model the system with one-to-one mapping. An inverse Duhem operator is proposed to extract the dynamic property of the hysteresis. Moreover, it can transform the multi-valued mapping of the hysteresis into a one-to-one mapping to suit the input of neural network. In order to compensate the effect of the hysteresis in piezoelectric actuator, the adaptive sliding mode controller with a feedforward hysteresis compensator is developed for the tracking control of the piezoelectric actuator. Experimental results demonstrate superior tracking performance, which validate the practicability and effectiveness of the presented approach.

Multiphysics Modeling and Material Selection Methods to Develop Optimal Piezoelectric Plate Actuators for Active Noise Cancellation

The performance of a piezoelectric actuator for active noise cancellation depends primarily on the quality of the actuator material and its design approach, i.e., single-layer or multi-layer actuators, stacks, benders, or amplified actuators. In this paper, material selection and multiphysics modeling were performed to develop an optimal piezoelectric plate actuator for active noise cancellation. The material selection process was analyzed using two multi-criteria decision making (MCDM) approaches for material selection, i.e., figure of merit (FOM) for actuators and the technique for order of performance by similarity to ideal solution (TOPSIS). Of the 12 state-of-the-art piezoelectric actuator materials considered in this article, PMN–28% PT is the best material according to TOPSIS analysis, while (PIN24%-PMN-PT) is the best material according to FOM analysis. The ranking of state-of-the-art piezoelectric material categories for actuators according to the two analysis is consistent and the category of monocrystalline piezoelectric materials has the highest actuation performance. The multiphysics modeling was performed using ANSYS Mechanical using two different approaches: one using Ansys Parametric Design Language (APDL) command fragments, the other installing the PiezoAndMEMS ACT extension in ANSYS. Static structure, modal, and harmonic response analyses were performed to determine an optimal pair of piezoelectric plates to be used as an actuator for active noise cancellation. A pair of plates of the same materials, but of different dimensions turns out to be the optimal piezoelectric plate actuator for active noise reduction, according to the two multiphysics modeling methods.