基于SVM的小型无人直升机飞行稳定性研究

Research on Flight Stability for a Model-Scale Helicopter Based on SVM

针对无人直升机控制中的滞后问题,文章提出了一种解决方法:先提取控制信号的特征值,然后利用特征值对无人机的进行判断,控制系统可以根据判断结果提前纠正系统的控制偏差。由于直升机飞行失败的样本极少,在稳定判断中引入了一个新的模式识别方法—支持向量机。支持向量机基于结构风险最小化原则,解决了小样本数据分类和泛化问题。文中在对支持向量分类机的原理进行了简单的介绍后,利用支持向量机和神经网络对直升机的飞行数据进行了分类,试验结果表明支持向量机具有较好的分类效果。

The paper presents a method to solve the lag problem of a model-scale helicopter,which firstly extracts the control signal＇s features,and then judges the flight stability,at last ineorreets the control error, A stability classification method based on support vector machine is proposed.SVM can solve small sample problems and has good generalization ability using the principles of structural risk minimization.After a simple introduction to the principles of the method,the classification test is explained in detail;what＇s more,several neural network methods are tested.Test results show that SVM has good performance to solve this kind of problem.