Poly(vinylidene fluoride), PVDF, as one of important polymeric materials with extensively scientific interests and technological applications, shows five crystalline polymorphs with α, β, γ, δ and e phases obtained by different processing methods. Among them, β phase PVDF presents outstanding electrical characteristics including piezo-, pyro-and ferroelectric properties. These electroactive properties are increasingly important in applications such as energy storage, spin valve devices, biomedicine, sensors and smart scaffolds. This article discusses the basic knowledge and character methods for PVDF fabrication and provides an overview of recent advances on the phase modification and recent applications of the β phase PVDF are reported. This study may provide an insight for the development and utilization for β phase PVDF nanofilms in future electronics.

https://www.mdpi.com/2073-4360/10/3/228/pdf

Properties and Applications of the β Phase Poly(vinylidene fluoride).

Feedforward control of sound and vibration using a neural network-based control system is considered, with the aim being to derive an architecture/algorithm combination which is capable of supplanting the commonly used finite impulse response filter/filtered-x least mean square (LMS) linear arrangement for certain nonlinear problems. An adaptive algorithm is derived which enables stable adaptation of the neural controller for this purpose, while providing the capacity to maintain causality within the control scheme. The algorithm is shown to be simply a generalization of the linear filtered-x LMS algorithm. Experiments are undertaken which demonstrate the utility of the proposed arrangement, showing that it performs as well as a linear control system for a linear control problem and better for a nonlinear control problem. The experiments also lead to the conclusion that more work is required to improve the predictability and consistency of the performance before the neural network controller becomes a practical alternative to the current linear feedforward systems. >

Active control of vibration using a neural network

Smart panel with multiple decentralized units for the control of sound transmission. Part I: theoretical predictions

Large-area PVDF thin films have been prepared and characterized for quasi-static and high frequency dynamic strain sensing applications. These films are prepared using hot press method and the piezoelectric phase (beta-phase) has been achieved by thermo-mechanical treatment and poling under DC field. The fabricated films have been characterized for quasi-static strain sensing and the linear strain-voltage relationship obtained is promising. In order to evaluate the ultrasonic sensing properties, a PZT wafer has been used to launch Lamb waves in a metal beam on which the PVDF film sensor is bonded at a distance. The voltage signals obtained from the PVDF films have been compared with another PZT wafer sensor placed on the opposite surface of the beam as a reference signal. Due to higher stiffness and higher thickness of the PZT wafer sensors, certain resonance patterns significantly degrade the sensor sensitivity curves. Whereas, the present results show that the large-area PVDF sensors can be superior with the signal amplitude comparable to that of PZT sensors and with no resonance-induced effect, which is due to low mechanical impedance, smaller thickness and larger area of the PVDF film. Moreover, the developed PVDF sensors are able to capture both A(0) and S-0 modes of Lamb wave, whereas the PZT sensors captures only A(0) mode in the same scale of voltage output. This shows promises in using large-area PVDF films with various surface patterns on structures for distributed sensing and structural health monitoring under quasi-static, vibration and ultrasonic situations. (C) 2010 Elsevier B.V. All rights reserved.

Characterization of a large-area PVDF thin film for electro-mechanical and ultrasonic sensing applications

Smart structure with build-in sensor(s) and actuator(s) that can actively and adaptively change its physical geometry and properties has been considered one of the best candidates in vibration control applications. Implementation of neural networks to system identification and vibration suppression of a smart structure is conducted in this paper. Three neural networks are developed, one for system identification, the second for on-line state estimation, and the third for vibration suppression. It is shown both in analysis and in experiment that these neural networks can identify, estimate, and suppress the vibration of a composite structure by the embedded piezoelectric sensor and actuator. The controller is also shown to be robust to system parameter variations.

Vibration Control of Smart Structures by Using Neural Networks

Feedback control of free field structural radiation is considered. State equations are formulated with a transformation which decouples the acoustic power error criterion. Using the resultant equations, expressed in terms of “transformed mode” states, the order of the state equations can be significantly reduced at low frequencies. Two experimental implementations of feedback control strategies using shaped piezoelectric polymer film sensors to measure the transformed system states are described. The first of these is a simple analog implementation. The second implementation is in discrete time, where an adaptive algorithm for optimizing the weights of IIR filters for practical use is described. It is shown that by using the outlined control approach significant levels of low frequency acoustic power attenuation can be obtained with no control spillover and subsequent increase in higher frequency acoustic power output.

The use of optimally shaped piezo-electric film sensors in the active control of free field structural radiation, Part 2: Feedback Control

A neural network and adaptive algorithm suitable for driving non-linear feedforward control systems used with "smart" structures to actively control sound and vibration is presented. The neural network/algorithm combination can be viewed as an extension to the commonly used transversal filter/filtered-x LMS algorithm combination, in that the neural network is essentially a transversal filter with a non-linear hidden layer placed between the input delay chain and output ac cumulator. The algorithm accommodates the transfer function and time delay between the control signal output and error signal input. The arrangement is shown in simulation to be capable of sup pressing a primary distrubance, which is a non-linear function of the reference signal fed to the con trol filter, and of suppressing harmonics introduced into a system by a control actuator exhibiting non-linear performance characteristics. The results are compared to those similarly obtained using a linear adaptive control arrangement, which is i...

A Neural Network for Feedforward Controlled Smart Structures

The influence which a narrowband adaptive feedforward controller, operating in the presence of unreferenced random noise, has upon the frequency response characteristics of the controlled system away from the controller referenced frequency band is investigated. It is found experimentally that the adaptation of the controller weights is responsible for increasing the resonant peaks of the response and introducing new peaks in some instances. This phenomenon is examined analytically, where it is shown that the unreferenced frequency components can propagate through the adaptive algorithm error signal input and exert an influence on the controller output signal spectrum. This phenomenon is found to be most troublesome when there is a response peak near the feedforward system reference frequency. The only remedy for the problem is a reduction in the adaptive algorithm convergence coefficient to a value far below that dictated purely by stability considerations.

Scott D. Snyder

Papers

The use of optimally shaped piezo-electric film sensors in the active control of free field structural radiation, Part 2: Feedback Control

A Neural Network for Feedforward Controlled Smart Structures

Modification to Overall System Response When Using Narrowband Adaptive Feedforward Control Systems