Applied Physics Department, Cadi Ayyad University, Marrakesh 40000, Morocco.
Mathematics Department, Royal School of Aeronautics, Marrakesh 40000, Morocco.
Comput Intell Neurosci. 2019 Jul 24;2019:8925165. doi: 10.1155/2019/8925165. eCollection 2019.
This paper explores the model parameters estimation of a quadrotor UAV by exploiting the cooperative particle swarm optimization-cuckoo search (PSO-CS). The PSO-CS regulates the convergence velocity benefiting from the capabilities of social thinking and local search in PSO and CS. To evaluate the efficiency of the proposed methods, it is regarded as important to apply these approaches for identifying the autonomous complex and nonlinear dynamics of the quadrotor. After defining the quadrotor dynamic modelling using Newton-Euler formalism, the quadrotor model's parameters are extracted by using intelligent PSO, CS, PSO-CS, and the statistical least squares (LS) methods. Finally, simulation results prove that PSO and PSO-CS are more efficient in optimal tuning of parameters values for the quadrotor identification.
本文通过利用协同粒子群优化-布谷鸟搜索（PSO-CS）来探索四旋翼无人机的模型参数估计。PSO-CS 利用 PSO 和 CS 的社会思维和局部搜索能力来调节收敛速度。为了评估所提出方法的效率，将这些方法应用于识别四旋翼的自主复杂非线性动力学被认为是很重要的。在使用牛顿-欧拉形式定义四旋翼动力学建模之后，使用智能 PSO、CS、PSO-CS 和统计最小二乘法（LS）方法提取四旋翼模型的参数。最后，仿真结果证明 PSO 和 PSO-CS 在四旋翼识别的参数值优化调整方面更有效。
