Real-time nonlinear moving horizon observer with pre-estimation for aircraft sensor fault detection and estimation

This paper presents a real-time nonlinear moving horizon observer (MHO) with pre-estimation and its application to aircraft sensor fault detection and estimation. An MHO determines the state estimates by minimizing the output estimation errors online, considering a finite sequence of current and past measured data and the available system model. To achieve the real-time implementability of such an online optimization-based observer, 2 particular strategies are adopted. First, a pre-estimating observer is embedded to compensate for model uncertainties so that the calculation of disturbance estimates in a standard MHO can be avoided without losing much estimation performance. This strategy significantly reduces the online computational complexity. Second, a real-time iteration scheme is proposed by performing only 1 iteration of sequential quadratic programming with local Gauss-Newton approximation to the nonlinear optimization problem. Since existing stability analyses of real-time moving horizon observers cannot address the incorporation of the pre-estimating observer, a new stability analysis is performed in the presence of bounded disturbances and noises. Using a nonlinear passenger aircraft benchmark simulator, the simulation results show that the proposed approach achieves a good compromise between estimation performance and computational complexity compared with the extended Kalman filtering and 2 other moving horizon observers.