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<p>The establishment of accurate dynamic models for accelerometers is a fundamental prerequisite for analyzing their dynamic characteristics and compensating for dynamic errors. To address the challenge of identifying structural parameters in micro-electro-mechanical system (MEMS) accelerometers, this study investigates the potential application of the Model Reference Adaptive Control (MRAC) algorithm and proposes an MRAC-based dynamic parameter identification strategy. Specifically, this strategy achieves optimal identification of the accelerometer’s core mechanical parameters by online estimation of the parameters of a parallel adjustable second-order dynamic model. Prior to hardware implementation, a series of simulations were conducted to verify two key performance aspects of the algorithm: convergence stability under different excitation signals and adaptability to variations in mechanical structural parameters. Subsequently, the algorithm was implemented in a digital accelerometer system based on a Field-Programmable Gate Array (FPGA). Experimental results indicated that the identification error of the sensor’s core mechanical parameters was <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\le 2.17\%\)</EquationSource> </InlineEquation>. To further validate the environmental robustness of the algorithm, additional experiments were performed under varying temperature conditions. The results demonstrated a high degree of consistency with trends derived from theoretical analysis, confirming that the proposed algorithm exhibits excellent stability and generalizability. This research provides critical technical support for the dynamic modeling and performance optimization of MEMS accelerometers.</p>

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MRAC-Based identification of structural parameters in MEMS accelerometer system

  • 期刊论文

  • 发表日期：

    2026年4月3日

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  • Qingbo Chu,
  • Dunzhu Xia,
  • Jinhui Li,
  • Xiuhua Yang

摘要

The establishment of accurate dynamic models for accelerometers is a fundamental prerequisite for analyzing their dynamic characteristics and compensating for dynamic errors. To address the challenge of identifying structural parameters in micro-electro-mechanical system (MEMS) accelerometers, this study investigates the potential application of the Model Reference Adaptive Control (MRAC) algorithm and proposes an MRAC-based dynamic parameter identification strategy. Specifically, this strategy achieves optimal identification of the accelerometer’s core mechanical parameters by online estimation of the parameters of a parallel adjustable second-order dynamic model. Prior to hardware implementation, a series of simulations were conducted to verify two key performance aspects of the algorithm: convergence stability under different excitation signals and adaptability to variations in mechanical structural parameters. Subsequently, the algorithm was implemented in a digital accelerometer system based on a Field-Programmable Gate Array (FPGA). Experimental results indicated that the identification error of the sensor’s core mechanical parameters was \(\le 2.17\%\) . To further validate the environmental robustness of the algorithm, additional experiments were performed under varying temperature conditions. The results demonstrated a high degree of consistency with trends derived from theoretical analysis, confirming that the proposed algorithm exhibits excellent stability and generalizability. This research provides critical technical support for the dynamic modeling and performance optimization of MEMS accelerometers.