Parameters Identification for a Composite Piezoelectric Actuator Dynamics

This work presents an approach for identifying the model of a composite piezoelectric (PZT) bimorph actuator dynamics, with the objective of creating a robust model that can be used under various operating conditions. This actuator exhibits nonlinear behavior that can be described using backlash and hysteresis. A linear dynamic model with a damping matrix that incorporates the Bouc–Wen hysteresis model and the backlash operators is developed. This work proposes identifying the actuator’s model parameters using the hybrid master-slave genetic algorithm neural network (HGANN). In this algorithm, the neural network exploits the ability of the genetic algorithm to search globally to optimize its structure, weights, biases and transfer functions to perform time series analysis efficiently. A total of nine datasets (cases) representing three different voltage amplitudes excited at three different frequencies are used to train and validate the model. Four cases are considered for training the NN architecture, connection weights, bias weights and learning rules. The remaining five cases are used to validate the model, which produced results that closely match the experimental ones. The analysis shows that damping parameters are inversely proportional to the excitation frequency. This indicates that the suggested hysteresis model is too general for the PZT model in this work. It also suggests that backlash appears only when dynamic forces become dominant

Sliding Mode-Based Robust Control for Piezoelectric Actuators with Inverse Dynamics Estimation

This paper presents an improved control approach to be used for piezoelectric actuators. The proposed approach is based on sliding mode control with estimation perturbation (SMCPE) techniques. Also, a proportional-integral-derivative (PID)-type sliding surface is proposed for position tracking. The proposed approach has been studied and implemented in a commercial actuator. A model for the system is introduced, which includes the Bouc-Wen (BW) model to represent the hysteresis, and it is identified by means of the System Identification Toolbox in Matlab/Simulink. Experimental data show that the proposed controller has a better performance when compared to a proportional-integral (PI) controller or a conventional SMCPE in motion tracking. Furthermore, a sub-micrometer accuracy tracking can be obtained while compensating for the hysteresis effect.This research was partially funded by the Basque Government through the project ETORTEK KK-2017/00033, and by the UPV/EHU through the projects PPGA18/04 and UFI 11/07

Precision Control of Piezo Electric Actuator

芝浦工業大学201

From model-driven to data-driven : a review of hysteresis modeling in structural and mechanical systems

Hysteresis is a natural phenomenon that widely exists in structural and mechanical systems. The characteristics of structural hysteretic behaviors are complicated. Therefore, numerous methods have been developed to describe hysteresis. In this paper, a review of the available hysteretic modeling methods is carried out. Such methods are divided into: a) model-driven and b) datadriven methods. The model-driven method uses parameter identification to determine parameters. Three types of parametric models are introduced including polynomial models, differential based models, and operator based models. Four algorithms as least mean square error algorithm, Kalman filter algorithm, metaheuristic algorithms, and Bayesian estimation are presented to realize parameter identification. The data-driven method utilizes universal mathematical models to describe hysteretic behavior. Regression model, artificial neural network, least square support vector machine, and deep learning are introduced in turn as the classical data-driven methods. Model-data driven hybrid methods are also discussed to make up for the shortcomings of the two methods. Based on a multi-dimensional evaluation, the existing problems and open challenges of different hysteresis modeling methods are discussed. Some possible research directions about hysteresis description are given in the final section

Analysis and control of monolithic piezoelectric nano-actuator

The study of the monolithic piezoelectric actuator, the dominant type of micropositioner is an attractive and challenging area, where realtime control theory and digital signal processing are effectively applied. The actuator can be applied in precision instruments and precision control, such as microscopes, medical and optics instruments because of the piezoelectric ceramic\u27s high resolution, fast transient response, and potential low cost. However, hysteresis nonlinearity and lightly damped vibration exist in the system, which limit the actuator applications. This work focuses on the hysteresis characteristics in time and frequency domains along with experimental and simulated results to verify the effectiveness of the model in describing the hysteresis phenomena. The analytic expressions of the hysteresis harmonics are further applied in hysteresis parameter estimation. A reduced order nonlinear hysteresis observer compensator is proposed, and the stability of the compensated system is discussed. The compensator reduces the hysteresis effect significantly under simulated and experimental conditions. Furthermore, an adaptive hysteresis observer compensator is further presented to compensate the slowly changed hysteresis parameters. Time division multi-control strategy is proposed to implement fast transient response, low vibration and high resolution. Extensive numerical simulation and real-time experiment are carried out to verify the control strategies. GUI is developed to implement the communication between the code in DSP memory and Labview, which improves the efficiency in system test

Design and experimental validation of a piezoelectric actuator tracking control based on fuzzy logic and neural compensation

This work proposes two control feedback-feedforward algorithms, based on fuzzy logic in combination with neural networks, aimed at reducing the tracking error and improving the actuation signal of piezoelectric actuators. These are frequently used devices in a wide range of applications due to their high precision in micro- and nanopositioning combined with their mechanical stiffness. Nevertheless, the hysteresis is one the main phenomenon that degrades the performance of these actuators in tracking operations. The proposed control schemes were tested experimentally in a commercial piezoelectric actuator. They were implemented with a dSPACE 1104 device, which was used for signal generation and acquisition purposes. The performance of the proposed control schemes was compared to conventional structures based on proportional-integral-derivative and fuzzy logic in feedback configuration. Experimental results show the advantages of the proposed controllers, since they are capable of reducing the error to significant magnitude orders.The authors wish to express their gratitude to the Basque Government, through the project EKOHEGAZ (ELKARTEK KK-2021/00092), to the Diputación Foral de Álava (DFA), through the project CONAVANTER, and to the UPV/EHU, through the project GIU20/063, for supporting this work

Robust fractional-order fast terminal sliding mode control with fixed-time reaching law for high-performance nanopositioning

Open Access via the Wiley Agreement ACKNOWLEDGEMENTS This work is supported by the China Scholarship Council under Grant No. 201908410107 and by the National Natural Science Foundation of China under Grant No. 51505133. The authors also thank the anonymous reviewers for their insightful and constructive comments.Peer reviewedPublisher PD

Hysteresis Characterization andCompensation of Smart Material-BasedActuators via a New Modified Bouc-WenModel

芝浦工業大学2016年

Feedforward Compensation Analysis of Piezoelectric Actuators Using Artificial Neural Networks with Conventional PID Controller and Single-Neuron PID Based on Hebb Learning Rules

This paper presents a deep analysis of different feed-forward (FF) techniques combined with two different proportional-integral-derivative (PID) control to guide a real piezoelectric actuator (PEA). These devices are well known for a non-linear effect called “hysteresis” which generates an undesirable performance during the device operation. First, the PEA was analysed under real experiments to determine the response with different frequencies and voltages. Secondly, a voltage and frequency inputs were chosen and a study of different control approaches was performed using a conventional PID in close-loop, adding a linear compensation and a FF with the same PID and an artificial neural network (ANN). Finally, the best result was contrasted with an adaptive PID which used a single neuron (SNPID) combined with Hebbs rule to update its parameters. Results were analysed in terms of guidance, error and control signal whereas the performance was evaluated with the integral of the absolute error (IAE). Experiments showed that the FF-ANN compensation combined with an SNPID was the most efficient.The authors wish to express their gratitude to the Basque Government through the project SMAR3NAK (ELKARTEK KK-2019/00051), to the Diputación Foral de Álava (DFA) through the project CONAVAUTIN 2 and to the UPV/EHU for supporting this work