Design and Implementation of a FPGA Anti-Handshaking Position Control Chip for a Voice Coil Motor

• Main Authors: Yi-Chao Lu, 呂宜釗
• Other Authors: Shir-Kuan Lin
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/78623059905072336853

碩士 === 國立交通大學 === 電機與控制工程系所 === 96 === In this thesis, the voice coil motor (VCM) with fast response time is used to be the actuator of the auto-focusing (AF) system applying to the digital camera or digital video camera. Because of the user’s handshaking, the AF apparatus induce the friction and inclination problem would cause incorrect position control. In order to overcome the effect of the friction and inclination, the disturbance observer and anti-windup with PI controller feedback system is applied to implement this anti-handshaking position control. The anti-handshaking position control of the VCM is realized on a chip based on FPGA (Field-Effective Programmable Gate Array) developed by Altera, and logic chips are integrated on a single FPGA chip. In digital logic chips, HDL (Hardware Description Language) is used to realize the function modules. For example, digital/analog conversion control, analog/digital conversion control, digital filter, proportional-integral (PI) controller for velocity loop, proportional (P) controller for position loop, disturbance observer, anti-windup controller, and position estimation algorithm. To realize normalization, 24 bits Q17 numerical format is used on numerical system design, and the problem of floating point number calculation on chip can be solved. Moreover, the precision of motor control and the resolution of chip data process can both be improved. Besides, the mini magnetic scale, mini magneto-resistive (MR) sensor and position estimation algorithm instead of using expensive interpolation chip are used for cost-down and mini-size. In the experiment system, Cyclone II FPGA Development Board is used to be the VCM control core, accompanying with a self-designed VCM Circuit Board consist of signal conversion, signal driving, and signal processing. The better system control performance can be achieved by measuring experiment data and modulating the control parameters.