半主动油气悬架的神经网络模型参考控制

以提高平顺性为目的,针对油气悬架系统刚度及阻尼非线性的特点,提出了以天棚阻尼为参考模型的神经网络控制方法.建立了二自由度的非线性油气悬架模型,分析了控制系统结构以及神经网络辨识器和控制器的设计与实现.以D级路面作为随机路面输入,分别在满载和空载下,对控制器的性能进行仿真研究.仿真结果表明,神经网络模型参考控制能够有效地衰减车身振动,提高行驶平顺性;并对控制对象的参数变化有良好的适应性.

Neural Network Model Reference Control for Semi-Active Hydro-Pneumatic Suspension

To improve riding comfort of vehicles,a neural network control strategy with sky-hook as reference model is put forward to deal with the non-linear characteristics of hydro-pneumatic suspension system.On the basis of 2-dof non-linear hydro-pneumatic suspension model,the neural control system’s structure was analyzed,and the neural network identifier and controller were designed.Taking the D-class road profile as random road input,through simulation,the performance of the control system was studied with full-load and non-load separately.The result shows that neural network model reference control strategy can effectively decrease the vibration of vehicle body,improve the ride comfort ability and have a good adaptability to the parameter change of the controlled object.