Abstract
This paper presents a novel multi-objective parameter optimization method based on the genetic algorithm (GA) and adaptive moment estimation (Adam) algorithm for the design of a closed-loop control system for the sense mode of a Microelectromechanical systems (MEMS) gyroscope. The proposed method can improve the immunity of the control system to fabrication tolerances and external noise. The design procedure starts by deriving a parameterized model of the closed-loop of the sense mode. The loop parameters are then optimized by the GA. Finally, the ensemble of optimized loop parameters is tested by Monte Carlo analysis to obtain a robust optimal solution. Simultaneously, the Adam-least mean square (LMS) demodulator, which is appropriate for the demodulation of very noisy signals, is also presented. Compared with the traditional method, the time consumption of the design process is reduced significantly. The digital control system is implemented by the print circuit board based on embedded Field Programmable Gate Array (FPGA). The experimental results show that the optimized control loop has achieved a better performance, the system bandwidth in open-loop and optimal closed-loop control system is about 23 Hz and 101 Hz, respectively. Compared to a non-optimized closed-loop system, the bias instability reduced from 0.0015°/s to 7.52 × 10−4°/s, the scale factor increased from 17.7 mV/(°/s) to 23 mV/(°/s) and the non-linearity of the scale factor reduced from 0.008452% to 0.006156%.
Highlights
Microelectromechanical systems (MEMS) gyroscopes are widely used in various fields such as navigation, guidance and other measurement applications owing to low power consumption, low cost, and their small size [1]
This paper proposes a multi-objective parameter optimization method based on a genetic algorithm for the design of the closed control loop and a least mean square (LMS) demodulator with adaptive moment estimation (Adam) optimization algorithm, and the robustness of the optimized parameters has been analyzed by the Monte Carlo method
By using the genetic algorithm, the optimized solutions within a specified parameter range can be obtained; here, the integral of time and absolute error (ITAE) of the control loop which contains settling time and steady-state error are selected as the index of performance evaluation
Summary
Microelectromechanical systems (MEMS) gyroscopes are widely used in various fields such as navigation, guidance and other measurement applications owing to low power consumption, low cost, and their small size [1]. The optimization method for MEMS gyroscope closed-loop control parameters is based on root locus techniques or directly on the transfer function [26,27]. These approaches suffer from two disadvantages: 1. In the control loop of the sense mode, the Adam-LMS demodulator can extract the in-phase Coriolis signal and quadrature signal respectively. As the final output signal of the closed control loop of sense mode, the angular rate output signal is shown on the computer through RS232
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