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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >SAW Torque Sensor Gyroscopic Effect Compensation by Least Squares Support Vector Machine Algorithm Based on Chaos Estimation of Distributed Algorithm
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SAW Torque Sensor Gyroscopic Effect Compensation by Least Squares Support Vector Machine Algorithm Based on Chaos Estimation of Distributed Algorithm

机译：基于分布式算法混沌估计的最小二乘支持向量机算法补偿声表面波转矩传感器陀螺效应

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摘要

As this study examined the issue of surface acoustic wave (SAW) torque sensor which interfered in high rotational speed, the gyroscopic effect generated by rotation was analyzed. Firstly, the SAW coupled equations which contained torque and rotation loads were deduced, and the torque calculation error caused by rotation was solved. Following this, the hardware of the SAW gyroscopic effect testing platform and the turntable experiment were designed to verify the correctness of the theoretical calculation. Finally, according to the experimental data, the gyroscopic effect was compensated by multivariate polynomial fitting (MPF), Gaussian processes regression (GPR), and least squares support vector machine algorithms (LSSVM). The comparison results showed that the LSSVM has the obvious advantage. For improving the function of LSSVM model, chaos estimation of distributed algorithm (CEDA) was proposed to optimize the super parameters of the LSSVM, and numerical simulation results showed that: (1) CEDA is superior to traditional estimation of distributed algorithms in convergence speed and anti-premature ability; (2) the performance of CEDA-LSSVM is better than genetic algorithms (GA)-LSSVM and particle swarm optimization (PSO)-LSSVM. After compensating by CEDA-LSSVM, the magnitude of the torque calculation relative error was 10−4 in any direction. This method has a significant effect on reducing gyroscopic interference, and it lays a foundation for the engineering application of SAW torque sensor.
机译：由于这项研究研究了表面声波（SAW）扭矩传感器在高速旋转时的干扰问题，因此分析了旋转产生的陀螺效应。首先，推导了包含转矩和旋转载荷的声表面波耦合方程，解决了旋转引起的转矩计算误差。在此基础上，设计了声表面波陀螺仪效果测试平台的硬件和转台实验，验证了理论计算的正确性。最后，根据实验数据，通过多元多项式拟合（MPF），高斯过程回归（GPR）和最小二乘支持向量机算法（LSSVM）补偿了陀螺效应。比较结果表明，LSSVM具有明显的优势。为了改善LSSVM模型的功能，提出了一种分布式算法混沌估计（CEDA）来优化LSSVM的超级参数，数值仿真结果表明：（1）CEDA在收敛速度和收敛速度上均优于传统的分布式算法。抗早衰能力； （2）CEDA-LSSVM的性能优于遗传算法（GA）-LSSVM和粒子群优化（PSO）-LSSVM。经CEDA-LSSVM补偿后，任意方向的扭矩计算相对误差为10 -4 。该方法对减少陀螺干扰有显著作用，为声表面波扭矩传感器的工程应用奠定了基础。

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