基于改进CPSO算法的四旋翼无人机姿态控制参数优化方法

Attitude Control Parameter Optimization Method for Quadrotor UAV Based on Improved CPSO Algorithm

摘要:

针对四旋翼无人机控制器参数较多且难以获得最优整定解等问题，提出了四旋翼无人机姿态控制参数的改进混沌粒子群优化算法。采用多维混沌映射函数产生初始化种群，提高初代种群质量；提出一种全域模糊扰动+邻域精准扰动的多维组合混沌扰动策略，可生成优质混沌扰动粒子；为同时兼顾时频域指标要求，设计了多性能指标约束条件下的加权性能指标函数,以确保优化结果能够满足各项指标要求。另外，进行了四旋翼无人机姿态控制数字仿真，统计结果表明改进策略可以产生较优初代种群，且性能指标在第4代即可快速收敛至近似最优解附近，六自由度仿真验证了改进策略的正确性和有效性。

Abstract:

In order to solve the problem that quadrotor UAV controller has many parameters and it is difficult to obtain the optimal tuning solution, an improved chaos particle swarm optimization algorithm for attitude control parameters of quadrotor UAV is proposed. The multi-dimensional chaotic mapping function is used to generate the initialized population, improve their quality. A multi-dimensional combinatorial chaotic disturbance strategy of global fuzzy perturbation + neighborhood precise perturbation is proposed, which can generate high-quality chaotic perturbation particles. In order to take into account the requirements of both time and frequency domain indicators, a weighted performance index function under the constraints of multiple performance indicators is designed to ensure that the optimization results can meet the requirements of various indicators. In addition, the digital simulation of attitude control of quadrotor UAV is carried out, and statistical results show that the improved strategy can produce a better first-generation population. The performance index can quickly converge to near approximate optimal solution in fourth generation, and the six-degree-of-freedom simulation verifies the correctness and effectiveness of the improved strategy.