Full-state-feedback, Fuzzy type I and Fuzzy type II control of MEMS accelerometer

This paper presents classic and knowledge-based intelligent controllers for regulation of a vibratory MEMS accelerometer. The proposed methods comprise Fuzzy type I (FTI), Fuzzy type II (FTII) and Full-state-feedback (FSF) control systems. An ideal model of sensor under FSF controller is used to generate the required reference data to train if-then rule-base and Membership functions (MFs) of both fuzzy controllers. Through feeding the reference data as well as the FTI/FTII output into an Adaptive neural fuzzy inference system (ANFIS), the rules and MFs of the FTI/FTII system are updated. The control systems are realized by adding a Kalman filter (KF) loop to the force-balancing method for estimation of state variables and input acceleration. Stochastic noises are filtered out while keeping good tracking performance of MEMS accelerometer and reducing the displacement of the mass under the closed-loop ANFIS-KF structure