Piezoelectric-actuated drop-on-demand droplet generator control using adaptive wavelet neural network controller

This paper presents the design, fabrication and control of a piezoelectric-type droplet generator which is applicable for on-line dispensing. 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 the stability of the closed-loop system can be assured. Unlike open-loop dispensing system, the system proposed can potentially generate droplets with high accuracy. Experimental verifications focusing on regulating 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. In order to illustrate the effectiveness of the proposed method, experimental results obtained using the AWNN scheme are compared with their counterparts using traditional PID control. The results indicate that the proposed AWNN scheme not only outperforms PID control but also works well in developing the piezoelectric-actuated drop-on-demand dispensing system. The proposed dispensing system provides droplet chains with an averaged mass as small as 31.5 mg while the associated standard deviation is as low as 0.72%.