【摘要】 微型碳膜位移传感器具有结构紧凑、可靠、低成本等诸多优点，在农业机械、机器人末端执行器、医疗手术器械等领域具有广阔的应用前景。由于碳膜厚度制造误差，导致微型碳膜位移传感器存在固有非线性，影响其测量精度。针对微型位移传感器固有非线性校正问题，采用神经网络方法，构建非线性校正模型，对传感器固有非线性进行校正。通过仿真与实验相结合的方法，从校正精度、实时解算速度2个维度，将神经网络非线性校正模型和现有PCM、BCM模型进行对比研究。研究结果表明，增加模型阶数，可以有效提高校正精度。对于BCM和神经网络非线性校正模型而言，三阶模型即可实现精度收敛。经过三阶PCM、BCM和神经网络非线性模型校正，传感器测量误差可分别降低46.1%、89.0%和89.6%。因此，神经网络非线性校正模型具有更高的校正精度。此时，PCM、BCM和神经网络非线性校正模型实时解算时间分别为0.48、0.49、0.85 ms,能够基本满足5 ms级高性能控制器应用需求。更多还原

【Abstract】 Micro carbon film displacement sensor has a broad application background in the fields of agricultural machinery, robot end effectors, medical and surgical instruments due to its advantages of compact structure, stability, low cost and so on. Due to the manufacturing error of carbon film thickness, the micro carbon film displacement sensor has an inherent nonlinearity, which could greatly affect its measurement accuracy. To calibrate the inherent nonlinearity of the micro displacement sensor, a Neural Network based Calibration Method(NNCM) is presented. By employing a methodology that combines simulation with experimentation, a comparative study was conducted to evaluate the NNCM against existing PCM and BCM across two dimensions, those are correction accuracy and real-time computing speed. Results reveal that increasing the model order can effectively enhance the calibration accuracy. For the BCM and NNCM, convergence in accuracy was achieved with the third-order model. Through the third-order PCM, BCM, and NNCM calibration, the measurement errors of the sensor were reduced by 46.1%, 89.0%, and 89.6% respectively. Therefore, the NNCM demonstrated the better precision in nonlinearity calibration. The real-time computing costs of PCM, BCM, and NNCM are 0.48 ms, 0.49 ms, and 0.85 ms, respectively, which could generally meet the requirements for applications with high-performance controllers at the 5 ms level.更多还原