A highly precise FADS (flush air-data sensing system) algorithm

The existing flush air data sensing systems have some deficiencies such as singularity values in calculating air data. Hence we propose a flush air data sensing algorithm based on the pseudo-inverse matrix and back-propagation (BP) neural networks, which we believe can overcome the deficiencies. The core of the algorithm consists of: (1) it uses the three-point method to estimate the local angle of attack and sideslip of an aircraft and diagnose its faults at pressure points; it then uses the pseudo-inverse matrix with fault tolerance to solve the total pressure and amend the dynamic pressure; (2) it utilizes the strong nonlinear mapping capability of the BP neural networks to fit the nonlinear mathematical model of the flush air-data sensing system, thus reducing the number of dimensions of input vectors and the level of difficulty in training networks and achieving the measurement calibration. The simulation results, given in Tables 1 and 2, and their analysis show preliminarily that our new algorithm has better fault tolerance and can produce highly precise and reliable air data.