Sensing And Control Of Mems Accelerometers Using Kalman Filter

Surface micromachined low-capacitance MEMS capacitive accelerometers which integrated CMOS readout circuit generally have a noise above 0.02g. Force-to-rebalance feedback control that is commonly used in MEMS accelerometers can improve the performances of accelerometers such as increasing their stability, bandwidth and dynamic range. However, the controller also increases the noise floor. There are two major sources of the noise in MEMS accelerometer. They are electronic noise from the CMOS readout circuit and thermal-mechanical Brownian noise caused by damping. Kalman filter is an effective solution to the problem of reducing the effects of the noises through estimating and canceling the states contaminated by noise. The design and implementation of a Kalman filter for a MEMS capacitive accelerometer is presented in the thesis in order to filter out the noise mentioned above while keeping its good performance under feedback control. The dynamic modeling of the MEMS accelerometer system and the controller design based on the model are elaborated in the thesis. Simulation results show the Kalman filter gives an excellent noise reduction, increases the dynamic range of the accelerometer, and reduces the displacement of the mass under a closed-loop structure.