无人机闭环控制系统局部飞行状态估计

Local Flight State Estimation for UAVs Closed-Loop Control System

以无人机六自由度非线性闭环系统模型为基础,选取易受传感器测量误差影响的气流角和机体旋转角速率作为关键系统状态,设计基于扩展卡尔曼滤波器的状态估计器,对强噪声条件下小型无人机闭环飞行控制系统的局部状态进行估计。在某无人机实时仿真平台上对所采用的方法进行了仿真实验,结果表明所提出的方法能够在低成本传感器典型噪声特性条件下实现对系统状态进行有效估计,闭环控制系统对突变指令的响应时间小于1 s,跟踪误差不大于1. 16%。

Based on the six-degree-of-freedom nonlinear closed-loop system model of a UAV,the airflow angle and the body angular rate were selected as the key states.And the state estimator based on extended Kalman filter(EKF)was designed to obtain the closed-loop system state estimation.The simulation experiments were performed on a Matlab/Simulink simulation platform of a UAV.The results show the proposed method can effectively estimate the state under the typical noise characteristics of low cost sensor.The response time of the closed-loop control system to the mutation command is less than 1 s;the tracking error is less than 1.16%.