摘要
针对无人机(unmanned aerial vehicles,UAVs)在编队形成过程中节省能量的问题,提出一种具有模糊约束的分布式模型预测控制算法。首先,用模糊数学理论把僚机相对长机的状态误差空间划分成多个模糊集,根据各僚机的状态误差设计速度和航向角指令的模糊约束;其次,把各僚机相对长机的模糊约束作为自身在分布式模型预测控制算法中的约束条件,以降低速度和航向角的变化幅度,使UAV在编队控制中节省能量;最后,与无模糊约束的分布式模型预测控制算法对比仿真。统计结果表明,该方法可缩减飞行路程、减小速度与航向角的变化累计值,起到节省能量的效果。
A distributed model predictive control algorithm with fuzzy constraints is proposed to solve the problem of energy saving in the formation of unmanned aerial vehicles(UAVs).Firstly,the state error space of the wingman relative to the leader is divided into multiple fuzzy sets by using the fuzzy mathematics theory,and the fuzzy constraints of speed and yaw angle commands are designed according to the state errors of each wingman.Secondly,the fuzzy constraints of each wingman relative to the leader are taken as its own constraints in the distributed model predictive control algorithm.By reducing the variation range of speed and yaw angle,the UAV can save energy in formation control.Finally,the proposed algorithm is verified by simulation through compared with the distributed model predictive control algorithm without fuzzy constraints.The statistical results show that this method can shorten the flight distance,reduce the cumulative value of speed and yaw angle change,and has the effect of saving energy.
作者
郝文康
陈琪锋
HAO Wenkang;CHEN Qifeng(School of Automation,Central South University,Changsha 410083,China)
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2024年第3期1021-1030,共10页
Systems Engineering and Electronics
关键词
无人机编队
分布式
模糊理论
模型预测控制
节能
unmanned aerial vehicles(UAVs)formation
distributed
fuzzy theory
model predictive control
energy-saving
