基于BP神经网络的静液压变速器控制研究

静液压变速器(HST)的操控性是农用车辆性能提升的关键,采用一种基于BP(back propagation)神经网络的新型控制策略,对HST马达输出转速的动态特性进行研究.基于变量泵—定量马达静液压传动系统的数学模型,首先对比研究了传统PID控制、模糊控制以及BP神经网络控制3种方法的控制效果,结果表明:与传统PID控制和模糊控制相比,BP神经网络控制能有效抑制系统超调量并降低马达转速波动,减小系统达到稳态的调节时间,具有良好的鲁棒性.基于此,提出采用BP神经网络控制方法对具有更大马达转速变化范围的变量泵—变量马达传动系统进行调查,研究结果表明,在对变量泵、变量马达分段控制中,该方法能实现较稳定的切换效果;在不同的负载等效转动惯量下,马达转速均能达到稳定状态,且由负载引起的转速波动也得到降低.研究结果表明,BP神经网络控制方法对变量泵—变量马达传动系统具有潜在的控制优势.

Control of hydrostatic transmission based on BP neural network

The maneuverability of hydrostatic transmission (HST) is a key factor in improving the performance of agricultural vehicles. In this paper, a new control strategy based on BP (back propagation) neural network is applied to study the dynamic characteristics of the output speed of HST motor. First, the control effects of traditional PID, fuzzy and BP neural network are compared based on the mathematical model of variable pump-quantitative motor. The results indicated that compared to traditional PID control and fuzzy control, BP neural network control can not only effectively suppress the overshoot of the system, but also reduce the fluctuation of motor speed and the adjustment time for system to reach stability. And it has excellent robustness. Therefore, the BP neural network is suggested in this paper to investigate the control effect of the variable pump-variable motor system (VPVM system) with larger motor speed variation range. The results show that this method can be used to achieve stable switching effect and reduce performance loss in segmented control of variable pump and variable motor. For the cases of different equivalent moment of inertia of load, all the motor speed can reach a stable state and the speed fluctuation caused by the load is reduced. The results indicate that BP neural network has a potential advantage in VPVM system control.