Virtual Sensor for the Angle-of-Attack Signal in Small Commercial Aircraft

An aircraft carries on board many sensors which measure a wide variety of variables. Due to the relations between the measured signals, a certain level redundancy is available. This redundancy can be used to estimate a particular variable based on signals that represent other variables. Such an estimator can be used as a virtual sensor. This paper describes the design of a virtual sensor for the Angle-of-Attack signal in a small commercial aircraft. In order to effectively use all available knowledge and data, and to comply with the stringent design requirements, the virtual sensor combines a number of technologies: a white-box linear time-varying model, a gray-box nonlinear Takagi-Sugeno (TS) fuzzy model and a black-box neural network compensator, whose purpose is to reduce the estimation error of the linear parameter varying model. The TS model and the neural network are trained by using data from nonlinear aircraft simulations. The inputs of the neural network are selected by a genetic search algorithm with a backward elimination procedure. Extensive evaluation has shown that the design requirements are amply met and that the proposed design methodology has a good potential for future applications in aircraft and other high-performance systems.