Real-Time Fault-Tolerant Moving Horizon Air Data Estimation for the RECONFIGURE Benchmark

Abstract

This paper proposes a real-time fault-tolerant estimation approach for combined sensor fault diagnosis and air data reconstruction. Due to simultaneous influence of winds and latent faults on monitored sensors, it is challenging to address the tradeoff between robustness to wind disturbances and sensitivity to sensor faults. As opposed to conventional fault-tolerant estimators that do not consider any constraints, we propose a constrained fault-tolerant estimator using moving horizon estimation (MHE). By exploiting wind bounds according to the weather or flight conditions, this approach improves fault sensitivity without sacrificing disturbance robustness. This improvement is attributed to active inequality constraints caused by faults, as shown in sensitivity analysis of the formulated MHE problem. The challenge of real-time nonlinear MHE is addressed by adopting an efficient structure-exploiting algorithm within a real-time iteration scheme. In order to facilitate the industrial validation and verification, the algorithm is implemented using an Airbus graphical symbol library to be compliant with the actual flight control computer, and its feasibility of real-time computation has been validated. The simulation results on the RECONFIGURE benchmark, which is a high-fidelity Airbus simulator, over a wide range of the flight envelop show the efficacy of the proposed approach.