基于神经网络的摩托车制动器试验台测控系统
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摘要
本文设计开发了摩托车制动器测试系统,将RBF神经网络与传统PID控制相结合,在控制过程中实时在线调整PID控制参数。试验表明,应用RBF神经网络的PID控制器能够获得更好的控制效果,并能很好的抑制现场工业干扰对系统的影响,提高了制动器台架测试系统的控制与测量精度和可靠性。
The motorcycle brake test rig was developed in this paper. The RBF neural network combined with classic PID control method was presented. In control process,the three PID parameters were adjusted online in real time. The experimental results demonstrated that control and measurement system based on RBF neural and PID can obtain much better control results. Moreover,industrial disturbance can be eliminate,the reliability and accuracy of control system were improved significantly.
The motorcycle brake test rig was developed in this paper. The RBF neural network combined with classic PID control method was presented. In control process,the three PID parameters were adjusted online in real time. The experimental results demonstrated that control and measurement system based on RBF neural and PID can obtain much better control results. Moreover,industrial disturbance can be eliminate,the reliability and accuracy of control system were improved significantly.
引文
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[2]张为春,曾祥军.基于模糊PID控制的汽车底盘模拟测功机测控系统[J].农业机械学报,2006,37(12):17~19.
[3]刘金琨.先进PID控制MATLAB仿真[M].北京:电子工业出版社,2004.
[4]吴广顺,凌雷,张立鹏.基于神经网络的传感器零点飘移补偿方法[J].现代测量与实验室管理,2018(6),16~17.
[5]梁波,李玉忍.模糊自整定PID在制动器试验台电惯量模拟应用[J].电子测量技术,2008,31(10):87~89.