A Research On The Control Algorithm And Realization Of A Miniature Inertial Sensor

Inertial measurement technology has several advantages, e.g. high autonomy, good real-time ability and capability of anti-interference. It manages the measurement and calculation of inertial factors. The core of the inertial measurement lies in the inertial measurement unit, which is an electronic device that measures both linear and angular acceleration of an object. Recently, with the development of material technology and digital circuits, the inertial measurement technology has developed a lot. But still, both reducing the size and improving the performance and precision of the inertial measurement unit are pretty difficult and challenging.The inertial sensing module is a new inertial measurement unit that combines together the capacitance sensor technology, the electromagnetic centering and digital closed-loop circuits. The inertial sensing module manages six inertial parameters measurement by a sensor. Moreover, it has a smaller size, lower power consumption and higher precision. The closed-loop feedback control algorithm is the key technology of the new inertial measurement unit.To both develop an available miniature inertial sensor and make software design of it, special attentions are paid on the PID algorithm and the Kalman filtering algorithm. Meanwhile, the performance of the algorithms is evaluated through theoretical analyses and simulation results. The final experimental result shows that the algorithms do work.The main contributions of this paper can be summarized as follows.1.This paper designs and realizes a capacitive sensing module. The module samples the capacitive parameters with high speed and accuracy. 2.This paper designs and realizes a current driver module. The module has high stability in a current source by precise control. Therefore, it is able to control the force on the coils.3. The PID algorithm enables feedback control and thus enables the IMU to manage the measurement of inertial parameters automatically. Meanwhile, with the help of Kalman filtering algorithm the measurement accuracy also improves.