A Design Methodology of Digital Control System for MEMS Gyroscope Based on Multi-Objective Parameter Optimization
A Design Methodology of Digital Control System for MEMS Gyroscope Based on Multi-Objective Parameter Optimization
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DOI：
10.3390/mi11010075
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发表时间：
2020-01-01
期刊：
影响因子：
3.4
通讯作者：
Liu, Xianxue
中科院分区：
文献类型：
--
作者：
Gu, Haoyu;Su, Wei;Liu, Xianxue
针对微机电系统（MEMS）陀螺仪检测模式的闭环控制系统设计，提出了一种基于遗传算法（GA）和自适应矩估计（Adam）算法的多目标参数优化方法。所提出的方法可以提高控制系统的制造公差和外部噪声的免疫力。设计过程开始于导出感测模式的闭环的参数化模型。然后用遗传算法对回路参数进行优化。最后，通过Monte Carlo分析对优化后的环路参数进行了测试，得到了一个鲁棒的最优解。同时，还介绍了适用于强噪声信号解调的亚当最小均方（LMS）解调器。与传统方法相比，设计过程的时间消耗明显减少。数字控制系统由基于嵌入式现场可编程门阵列（FPGA）的印刷电路板实现。实验结果表明，优化后的控制环具有较好的性能，开环和最优闭环控制系统的系统带宽分别为23 Hz和101 Hz。与未优化的闭环系统相比，偏置不稳定性从0.0015度/秒降低到7.52 × 10（-4）度/秒，比例因子从17.7 mV/（度/秒）增加到23 mV/（度/秒），比例因子的非线性从0.008452%降低到0.006156%。
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 degrees/s to 7.52 x 10(-4)degrees/s, the scale factor increased from 17.7 mV/(degrees/s) to 23 mV/(degrees/s) and the non-linearity of the scale factor reduced from 0.008452% to 0.006156%.