摘要
无人机控制策略采用俯仰高度控制、油门空速控制和侧偏距控制。在设计的适应度函数中引入高度误差、相角裕度和幅值裕度,利用粒子群算法迭代寻找适应度函数最优值确定控制器参数。对无人机纵向高度运动和横侧向位置运动进行仿真,通过各控制器的阶跃响应和开环频率特性曲线,验证了粒子群算法得到的控制器参数满足设计要求。在仿真中引入离散突风,进一步验证通过粒子群算法得到的控制器参数的可靠性,说明粒子群算法简捷、快速、可靠的优点。
Research on the parameter optimization design of longitudinal and lateral channel controller is de-veloped in this paper based on PSOCParticle Swarm Optimization)algorithm. Control strategies include the control of UAV ( unmanned aircraft vehicle) height, velocity and lateral offset. Height error, phase margin and magnitude margin are added to the fitness function to find the best value of fitness function based on PSO. Besides, simulations are completed for the longitudinal and lateral control of UAV to prove the effec-tiveness of controller parameters obtained via PSO by evaluating the step response and bode plots. Simula-tions under wind gust are completed as well to prove the reliability of controller parameters obtained by PSO and also the advantages of PSO such as simplicity, fastness,and reliability.
出处
《西安航空学院学报》
2017年第1期3-7,共5页
Journal of Xi’an Aeronautical Institute
关键词
粒子群优化算法
无人机高度控制
复杂气流扰动
PID控制结构
particle swarm optimization algorithm
UAV height control
complex airflow disturbance
PID control structure