基于混沌蜂群算法的小型无人直升机系统辨识

Chaotic Artificial Bee Colony Algorithm for the System Identification of a Small-Scale Unmanned Helicopter

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,提出了一种基于混沌蜂群算法(chaotic artificial bee colony algorithm,简称CABC)的辨识方法。由于直升机的数学模型是非线性的,因此用小扰动理论对其线性化,得到纵横方向待辨识的解耦模型;进一步将系统辨识问题转变成优化问题,以蜂群为搜索单位,通过群体之间的信息交流与优胜劣汰机制,使得蜂群向更优方向进化;利用混沌算子来改进侦察蜂的搜索机制,使得人工蜂群算法脱离局部最优束缚,获得更强的全局寻优能力。根据无人机实际飞行试验数据,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,估计出了解耦模型中的未知参数,与遗传算法和传统人工蜂群算法相比,所提算法的辨识精度更高。

Aimed at system identification of a small-scale unmanned helicopter in the hover condition,a novel chaotic artificial bee colony algorithm(ABC)is proposed.Due to the nonlinearity of the helicopter′s mathematical model,the helicopter′s dynamic model is converted to longitudinal and horizontal decoupled linear helicopter models based on the small disturbance theory.In addition,the system identification problem is turned into an optimization problem,the intelligent bee colony is employed to seek.The ABC algorithm can evolve in a better direction with the information exchanges among the colony and the survival of the fittest.The chaotic operator is added to the ABC algorithm to help it jump out of local optimum and improve its global search ability.The model is validated and analyzed through the actual flight data by system identification.The results show that unknown parameters can be estimated based on the proposed algorithm.Our proposed algorithm has greater accuracy than genetic and traditional algorithms.