Functional control of the technical condition method for aircraft control system sensors under complete parametric uncertainty

• Main Authors: J. V. Bondarenko, E. Yu. Zybin
• Format: Article
• Language: Russian
• Published: Moscow State Technical University of Civil Aviation 2020-07-01
• Series: Naučnyj Vestnik MGTU GA
• Subjects: aircraft; control system; sensors; health monitoring; localization and detection failure; parametric uncertainty; nonparametric method
• Online Access: https://avia.mstuca.ru/jour/article/view/1702

The control system sensors failures can cause the aircraft stability and controllability deterioration. Such failures fast and reliable inflight detection and localization allows minimization their consequences and prevention of an accident. Direct application of traditional parametric methods for sensors health monitoring with the use of their mathematical models is impossible due to the lack of information about the real inputs on their sensitive elements. This leads to the need for the problem of aircraft flight dynamics modeling with a high level of uncertainties to be solved, which complicates the application of functional test methods and determines the necessity of excessive sensors hardware redundancy. Widely known nonparametric methods either require a prior knowledge base, preliminary training, or long-term tuning on a large real flight data volume, or have low selective sensitivity for the failed sensors reliable localization. This paper expands the application of the well-known nonparametric failure detection criterion, based on the analysis of the linear dependence of the input-output data Hankel matrix columns and solution of the sensor failures localizing problem. Necessary and sufficient solvability conditions are given, the structure and the criterion values are determined in an analytical form before and after the failures occurrence. The proposed method does not require functional or hardware redundancy, prior information about the parameters of mathematical models and their stability, identification, observation, or prediction problems solution. The efficiency of the method is shown on the Boeing 747–100/200 longitudinal model example. Fast tuning, fast response and selective sensitivity of the developed algorithms are noted.