基于新型学习观测器的卫星执行机构故障重构

A novel learning observer-based fault reconstruction for satellite actuators

针对在轨微小卫星出现执行机构故障的情况,提出了一种基于非线性学习观测器(nonlinear learning observer, NLO)的卫星姿控执行机构故障重构方法。文中结合迭代学习算法和递推学习算法,设计了一种新型自适应学习算法,该算法应用前一时刻和当前时刻的姿态敏感器测量输出误差在线更新故障重构信号,使得所提NLO在估计卫星姿态角速度和姿态角的同时,能够快速精确在线重构卫星姿控执行机构故障。进一步给出了所提NLO的稳定性条件,并结合线性矩阵不等式技术给出了NLO增益矩阵的详细设计方法。最后,将所提方法应用于微小卫星姿控推力器故障重构,仿真结果验证了所提方法的有效性。

Considering actuator faults occur when a microsatellite runs on orbit, nonlinear learning observer (NLO)-based fault reconstruction for satellite attitude control systems is investigated. Combined with the advantages of iterative learning algorithm and recursive learning algorithm, a novel learning algorithm involving current and previous measurement output errors is first proposed such that the proposed NLO can estimate satellite attitude angles and attitude angular velocities and reconstruct actuator faults accurately and quickly. Further, the stability conditions of the proposed NLO are provided and detailed design of observer gain matrices is given using the linear matrix inequality technique. At last, the proposed approach is applied to reconstruct thruster faults in microsatellites, simulation results validate the effectiveness of the proposed fault reconstruction approach.