摘要
针对小型无人直升机在悬停状态下飞行动力学模型的系统辨识问题,提出了一种基于预测误差法与人工蜂群算法(PEM-ABC)结合的辨识算法。该算法将系统辨识问题转化为优化问题,用PEM算法确定搜索空间的范围;雇佣蜂搜索阶段采用改进的自适应搜索策略加快收敛速度;跟随蜂搜索阶段引入一种新的概率选择方式保证种群多样性;侦察蜂搜索阶段利用混沌算子来提高全局搜索能力。通过机载设备采集到的飞行实验数据,对辨识获得的模型进行了分析与验证。结果表明:采用该辨识方法,估计出了无人直升机动力学模型的未知参数,与PEM算法和传统人工蜂群算法相比,所提算法的辨识精度更高,具有重要的工程使用价值。
The small-scale unmanned helicopter is well-known by its hovering capabilities. However,it exhibits a nonlinear and complex dynamic phenomenon,and it is a loop unstable,high degree of inter axis coupling system. The goal of autonomous flight was realized based on an accurate and appropriate helicopter model. And system identification is the practical method to obtain the model. Aiming at the system identification of a small-scale unmanned helicopter in hover condition,a novel algorithm combined prediction error method with artificial bee colony algorithm（ PEM-ABC） was proposed. In the proposed algorithm,the problem of system identification was turned into an optimization problem. The search scope was arranged by the PEM algorithm; in that case,the initial solutions can be obtained. And in the stage of employed bee search,an adaptive search strategy was adopted to increase the speed of convergence. In the stage of following bee search,a new probability of selection strategy was introduced to keep the diversity of the population. And in the stage of scout bee search,the chaotic search operator was used to improve the ability of global search. Through the actual flight data collected by airborne equipment,the model used in system identification was validated and analyzed. The results show that the unknown parameters can be estimated based on the proposed algorithm. Compared to PEM algorithm and traditional ABC algorithm,the identified accuracy of the proposed algorithm was better,which showed an important engineering application value.
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2016年第1期8-14,共7页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家自然科学基金项目(51375230)
江苏省科技支撑计划重点项目(BE2013003-1
BE2013010-2)
关键词
小型无人直升机
系统辨识
预测误差法
人工蜂群算法
small-scale unmanned helicopter
system identification
prediction error method
artificial bee colony algorithm
