Research on Load Adaptive Control for High-Frequency Motion of Voice Coil Motor With Online Parameter Optimization

The fast tool servo system is a crucial intelligent equipment for workpiece machining and forming in the precision manufacturing field. The Voice Coil Motor (VCM), serving as the driving device of this equipment, offers advantages such as high-frequency response, high precision, large stroke, and low noise. However, during high-frequency reciprocating motion control, the VCM is prone to the influence of changes in factors like load, motion amplitude and frequency, which leads to a decline in motion accuracy. To address this issue, this research proposes an innovative adaptive control method based on online parameter optimization. By enabling the Back Propagation Neural Network (BPNN) to learn the key characteristics of the VCM’s driving current under different working conditions, and through continuous iterations during the training process of the network model, real-time online adjustment of controller parameters is achieved, thereby realizing the adaptive control of the VCM. Experimental results show that compared with classical PID control and fuzzy PID adaptive control, the proposed control method enables the VCM to achieve higher motion accuracy during high-frequency motion. Under the condition of a sinusoidal superimposed wave input with an amplitude of 0.1 mm and frequencies of 60 Hz and 120 Hz, when the load carried by the VCM is 200 g, the absolute peak error of the displacement under the proposed control reaches $3.5~\mu $ m.