先进战斗机过失速机动大气数据融合估计方法

可控的过失速机动是先进战斗机超机动性能的重要标志，飞机飞行包线的扩大已超出传统的大气数据系统测量范围，可靠的迎角、侧滑角、总压、静压等飞行大气数据是制约先进战斗机过失速机动中飞行控制的关键因素。以中国推力矢量验证机为对象，基于过失速机动飞行试验的数据，开展大气参数估计与验证研究。结合过失速机动的时间与空间特性，研究了基于风速、地速、空速矢量和惯性姿态、导航参数的大气参数融合计算方法；针对过失速大迎角状态下飞机周围气流非定常、模型非线性导致的融合大气参数误差的复杂特性，进一步构建深度神经网络，对机动状态融合迎角、侧滑角的强非线性误差进行拟合。仿真和飞行试验表明：该方法可在大迎角飞行状态下实现主要大气参数的融合估计，过失速机动过程中融合迎角误差优于2.3°，融合得到的大气参数可为过失速大迎角机动飞行控制提供可靠的大气参数状态反馈。

Air data fusion and estimation method for advanced aircrafts in post-stall maneuver

Post-stall maneuver capability is an important feature of advanced aircrafts, and the extension of flight envelope exceeded the measurement range of traditional air data systems. air data including angle of attack, angle of sideslip, total and static pressure are the essential factors for the control of advanced fighters in post-stall maneuver. An air data fusion and estimation method was proposed and validated based on a thrust vector technology test. The first step is to calculate air data by blending vectors of wind speed, ground speed, and navigation parameters such as attitude and rotation rate. A kind of deep neural network with multiple hidden layers and better feature expression and ability to model complex mapping was designed, trained and employed to fit the calculated angle of attack errors with strong nonlinearity and hysteresis, which were due to the unsteady flow around the aircraft and nonlinear relationship among the flight state parameters. Simulation and flight data shown that the proposed method can complete the estimation of air data even at high angle of attack maneuver and achieve angle of attack parameter with an error no more than 2.3°. The estimated air data can be provided to the flight control system as reliable state feedback.