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Development of a Piezoelectric-Actuated Drop-On-Demand Droplet Generator using Adaptive Wavelet Neural Network Control Scheme

机译:使用自适应小波神经网络控制方案开发压电驱动的滴滴液滴发生器

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This paper presents the design, fabrication and control of a piezoelectric-type droplet generator which is applicable for on-line dispensing. The piezoelectric-actuated dispensing system consists of a linear piezoelectric motor (LPM) actuated table, a plastic syringe, a nozzle, a linear encoder and a PC-based control unit. Adaptive wavelet neural network (AWNN) control is applied to overcome nonlinear hysteresis inherited in the LPM. The adaptive learning rates are derived based on the Lyapunov stability theorem so that convergence of the tracking error can be assured. Unlike open-loop dispensing system, the system proposed can potentially generate droplets with high accuracy. Experimental verifications including regulating and tracking control are performed firstly to assure the reliability of the proposed control schemes. Real dispensing is then conducted to validate the feasibility of the piezoelectric-actuated drop-on-demand droplet generator. The results demonstrate that the proposed scheme works well in developing the piezoelectric-actuated drop-on-demand dispensing system.
机译:本文介绍了一种压电型液滴发生器的设计,制造和控制,适用于在线分配。压电致动的分配系统由线性压电马达(LPM)致动桌,塑料注射器,喷嘴,线性编码器和基于PC的控制单元组成。应用自适应小波神经网络(AWNN)控制来克服LPM中遗传的非线性滞后。基于Lyapunov稳定性定理导出自适应学习速率,从而可以确保跟踪误差的收敛。与开环分配系统不同,所提出的系统可以潜在地产生高精度的液滴。首先执行包括调节和跟踪控制的实验验证,以确保所提出的控制方案的可靠性。然后进行真实的分配以验证压电致动滴管液滴发生器的可行性。结果表明,所提出的方案在开发压电驱动的按需分配系统方面运用。

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