基于MIT规则的自适应Unscented卡尔曼滤波及其在旋翼飞行机器人容错控制的应用

提出一种新颖的基于MIT规则的自适应Unscented卡尔曼滤波(Unscented Kalman filter,UKF)算法,用来进行参数以及状态的联合估计。针对旋翼飞行机器人执行器提出一种执行器健康因子(Actuator health coefficients,AHCs)的故障模型结构,应用自适应UKF对AHCs参数进行在线估计,将联合估计的状态以及故障参数引入基于模型的反馈线性化控制结构,组成完整的容错控制系统。提出的自适应UKF算法以及容错控制结构经过中科院沈阳自动化研究所ServoHeli-20旋翼无人智能平台数学模型进行仿真试验验证,效果良好。

A new fault tolerant control methodology against the actuator failure is proposed.The novel filter method with adaptability to statistical characteristic of noise is proposed to improve the estimation accuracy of Unscented Kalman filter(UKF).The actuator health coefficients(AHCs) is introduced to denote the actuator failure model while the adaptive UKF is employed for on-line estimation of both the flight states and the AHCs parameters of rotorcraft UAV(RUAV).Simulations are conducted by using the model of SIA ServoHeli-20 RUAV of Shenyang Institute of Automation, CAS, and the results are compared with those obtained by normal UKF to demonstrate the effectiveness and improvements.

国家高技术研究发展计划资助项目(863计划,2006AA04Z206)