基于模糊神经网络的体能训练仪器齿轮转速测量系统设计

Design of Gear Speed Measurement System for Physical Fitness Training Instrument Based on Fuzzy Neural Network

目前,我国对于体能训练愈加重视,因此,体育训练仪器的发展具有很好的前景,能帮助体育运动员得到进一步提升。其中对于齿轮转速测量是保证仪器正常运作的重要步骤。研究通过对电压型转子磁链估算模型的重新设计,改善了纯积分环节中的漂移和累积误差的问题。并且结合各个转速测量的优缺点,研究最终选取模糊理论与神经网络相结合的新型测量方法,对转速进行测量。实验在MATLAB平台上对1 000 r/min、1 200 r/min的初始转子速度分别进行了仿真实验以及实际应用。实验结果表明,模型在空载启动下,估算结果与实际值之间的误差在-0.04至0.04区间;在载人启动情况下,其误差值在-0.02至0.03。模型表现出较快的收敛速度,曲线也比较平缓,验证模糊神经网络模型在转速测量上的可行性。

At present,China attaches increasing importance to physical fitness training,so the development of sports training equipment has good prospects,which can help athletes further improve.The measurement of gear speed is an important step to ensure the normal operation of the instrument.The study improved the impact of pure integration on drift and cumulative error by redesigning the voltage type rotor flux estimation model.And based on the advantages and disadvantages of various speed measurements,a new measurement method combining fuzzy theory and neural network was ultimately selected to measure the speed.The initial rotor speeds of 1000r/min and 1200r/min were simulated and applied on the MATLAB platform.The experimental results show that the error between the estimated results and the actual values of the model under no-load startup is within the range of-0.04 to 0.04;In the case of manned start-up,the error value is between-0.02 and 0.03.The model exhibits a relatively fast convergence speed and a relatively flat curve,verifying the feasibility of the performance of the fuzzy neural network model in speed measurement.