Parametric topology optimization of a MEMS gyroscope for automotive applications

Abstract Automotive MEMS gyroscopes are used for various purposes, such as rollover prevention and dynamic stability. Although, employment of gyroscopes for automotive applications is reported, what is not discussed is the gyro topology design and optimization for these applications. This article reports parametric topology size optimization of a MEMS gyroscope proper for automotive applications. The structure optimization of a translational dual-mass Coriolis vibratory gyroscope (CVG) with electrostatically sense/excitation mechanism is presented for automotive applications. Fabrication considerations conform to the X-FAB procedures, and application uncertainty is inherent in the vehicle lateral model. The proposed design approach takes into account the inaccessible levels of application uncertainty to optimization, which provides results that are more reliable, compared with the ideal models. More than thirteen thousand virtual cases are studied, in which the gyroscope performance specifications including scale factor, linearity, etc. are calculated. The double-lane-change (DLC) maneuver based on ISO 3888-1 is performed on the optimal candidates in order to apply the vehicle application constrains. The suggested optimal solution is the one with the least yaw rate error with respect to the target yaw rate instructed by the maneuver, which suits best for the automotive application. The results show that the yaw rate error of DLC maneuver improved from about 40% to about 1.4% using the optimization algorithm, which shows the effectiveness of the proposed method.

[1]  Giulio Reina,et al.  Vehicle parameter estimation using a model-based estimator , 2017 .

[2]  Giulio Reina,et al.  Vehicle dynamics estimation via augmented Extended Kalman Filtering , 2019, Measurement.

[3]  S. E. Alper,et al.  Quadrature-Error Compensation and Corresponding Effects on the Performance of Fully Decoupled MEMS Gyroscopes , 2012, Journal of Microelectromechanical Systems.

[4]  Wing Kam Liu,et al.  A level set approach for optimal design of smart energy harvesters , 2010 .

[5]  Chong Shen,et al.  Note: Parallel processing algorithm of temperature and noise error for micro-electro-mechanical system gyroscope based on variational mode decomposition and augmented nonlinear differentiator. , 2018, The Review of scientific instruments.

[6]  Michael Kraft,et al.  Parameter optimization for a high-order band-pass continuous-time sigma-delta modulator MEMS gyroscope using a genetic algorithm approach , 2012 .

[7]  A. M. Shkel,et al.  Dual Foucault Pendulum gyroscope , 2015, 2015 Transducers - 2015 18th International Conference on Solid-State Sensors, Actuators and Microsystems (TRANSDUCERS).

[8]  Andrei M. Shkel,et al.  Micromachined rate gyroscope architecture with ultra-high quality factor and improved mode ordering , 2011 .

[9]  Giulio Reina,et al.  The FLEXnav precision dead-reckoning system , 2006 .

[10]  Weileun Fang,et al.  Design and implementation of a fully-decoupled tuning fork (FDTF) MEMS vibratory gyroscope for robustness improvement , 2015, 2015 Transducers - 2015 18th International Conference on Solid-State Sensors, Actuators and Microsystems (TRANSDUCERS).

[11]  Xu Guo,et al.  Explicit structural topology optimization under finite deformation via Moving Morphable Void (MMV) approach , 2019, Computer Methods in Applied Mechanics and Engineering.

[12]  Liu Jun,et al.  Temperature drift modeling of MEMS gyroscope based on genetic-Elman neural network , 2016 .

[13]  Hugh F. Durrant-Whyte,et al.  Inertial navigation systems for mobile robots , 1995, IEEE Trans. Robotics Autom..

[14]  Xingling Shao,et al.  Pole-Zero Temperature Compensation Circuit Design and Experiment for Dual-Mass MEMS Gyroscope Bandwidth Expansion , 2019, IEEE/ASME Transactions on Mechatronics.

[15]  Lihong Zhang,et al.  Structural optimization of Z-axis tuning-fork MEMS gyroscopes for enhancing reliability and resolution , 2015 .

[16]  Zhan Kang,et al.  Integrated topology optimization of multi-component structures considering connecting interface behavior , 2018, Computer Methods in Applied Mechanics and Engineering.

[17]  Keith J. Burnham,et al.  Dual extended Kalman filter for vehicle state and parameter estimation , 2006 .

[18]  Vladislav Apostolyuk,et al.  Coriolis Vibratory Gyroscopes , 2016 .

[19]  A.A. Trusov,et al.  Environmentally Robust MEMS Vibratory Gyroscopes for Automotive Applications , 2009, IEEE Sensors Journal.

[20]  Roland Siegwart,et al.  Computer Vision Methods for Improved Mobile Robot State Estimation in Challenging Terrains , 2006, J. Multim..

[21]  Bin Zhou,et al.  Optimal Design of a Center Support Quadruple Mass Gyroscope (CSQMG) † , 2016, Sensors.

[22]  Heung Seok Kang,et al.  Micro-accelerometer Based on Vertically Movable Gate Field Effect Transistor , 2015, Nano-micro letters.

[23]  K. Najafi,et al.  A HARPSS polysilicon vibrating ring gyroscope , 2001 .

[24]  Gary Witus,et al.  Terrain characterization and classification with a mobile robot , 2006, J. Field Robotics.

[25]  Xingling Shao,et al.  Sensing mode coupling analysis for dual-mass MEMS gyroscope and bandwidth expansion within wide-temperature range , 2018 .

[26]  Jinhao Sun,et al.  Design and optimization of a resonant output frequency gyroscope for robust sensitivity and bandwidth performance , 2016 .

[27]  Giulio Reina,et al.  Slip-based terrain estimation with a skid-steer vehicle , 2016 .

[28]  Rudra Pratap,et al.  Sensitivity analysis of an in-plane MEMS vibratory gyroscope , 2018 .

[29]  Usung Park,et al.  Tactical grade MEMS vibrating ring gyroscope with high shock reliability , 2015 .

[30]  Ruben Andres Salas,et al.  On response time reduction of electrothermomechanical MEMS using topology optimization , 2012 .

[31]  M. Madou.  Fundamentals of microfabrication and nanotechnology , 2012 .