基于路面自动识别的ABS自适应神经模糊控制器仿真研究

有效、快速的道路状况自动识别对于提高ABS性能具有重要意义。通过仿真试验分析,提出了一种比传统方法更快更高效的路面识别方法,并设计了以滑移率为控制目标的ABS模糊神经网络控制器。结合车辆模型熏对单一附着系数路面和变附着系数路面进行了ABS制动模拟试验。结果表明熏基于路面自动识别ABS模糊控制系统能快速、准确判断出路面状况的变化熏自动调整、优化控制器控制参数熏使车辆获得最大地面制动力,与传统利用车身加速度进行路面识别的逻辑门限控制器相比,该控制器反应更灵敏,控制更精确。

The Simulation Research of ABS Self-Adapting Nerve Fuzzy Control Unit Base on Road Automatic Identification

The effective and rapid road status automatic identification is very important to improve ABS capability.Through simulation test analysis,present a kind of faster and more effective identification method than conventional method,and design ABS nerve fuzzy control unit that aim at controlling slip ratio.Combine with vehicle model,simulate ABS brake test with single attachment coefficient road and variable attachment coefficient road.The result show that the system base on road automatic identification ABS fuzzy control can rapidly and accurately judge the variation of road status,self-adjustment?optimize the control parameter of control unit,and make the vehicle get the maximum road brake force,contrast to conventional logic gate control unit base on body accelerator road identification,the reaction of this control unit is more sensitive and the control is more accurate.