一类参数不确定非线性系统的故障检测与重构

摘要

飞行器在全包络上表现出明显的气动参数不确定性，以某无人机纵向模型为研究对象，提出一种不确定参数在线估计的自适应观测器故障重构方法。首先，将系统状态方程描述为一类带时变参数的仿射非线性结构，在参数增广系统能观性分析基础上，采用增广容积卡尔曼滤波（augmented cubature Kalman filter，ACKF）算法实现气动参数在线估计，以克服鲁棒性死区故障检测方法的保守性，提高检测灵敏度。其次，将所估计参数用于自适应观测器设计，由于 Lie 导数分析方法保证了对象系统的能观性，故系统不必满足文献方法中的特定规范形式；在此基础上，给出了故障检测自适应阈值和故障参数调节律，并分析了估计误差的收敛性。仿真实验表明了所提方法的有效性。%Aiming at the obvious aerodynamic parametric uncertainties of aircraft in the full flight envelope, a fault reconstruction method based on a modified adaptive observer is proposed.The adaptive observer is im-proved by the uncertain parameter estimation process and validated on the longitudinal model of some unmanned vehicle.According to this method,the longitudinal model is described as an affine nonlinear structure with time variant parameters,and the observability of its parameter-augmented model is analyzed.On this basis,to deal with conservatism of the robustness dead zone technique in fault detection and refine the detection sensitivity, aerodynamic parameters are identified on line by the augmented cubature Kalman filter (ACKF)algorithm. Then the parameter estimations are adopted in adaptive observer design.As the Lie derivative based criterion guarantees observability of the object system,the literature restriction that the object system must be within some particular structure is avoided.Based on this,the adaptive fault detection threshold and the fault parame-ter regulation law are derived,and the convergency of the estimation error is analyzed.Finally,simulations are conducted to testify the availability of this method.