Integrated Morphing Strategy and Trajectory Optimization of a Morphing Waverider and Its Online Implementation Based on the Neural Network

In previous studies, a variable-sweep-wing morphing waverider was proposed and its aerodynamic performance and dynamic model were provided. This morphing waverider can change sweep angle according to different flight scenario in order to achieve an optimal flight performance. For the traditional fixed-geometry waverider, the main objective of the trajectory planning problem is to find an optimal trajectory consisting of time-dependent variables including the angle of attack, the velocity, the altitude, the flight path angle and so on. As for this morphing waverider, an optimal morphing wing changing strategy is also required to be calculated together with an optimal trajectory. At first, to alleviate computational cost and improve trajectory-planning efficiency, aerodynamic coefficients of the morphing waverider are approximated and modeled as polynomial functions of multiple variables including the Mach number, the angle of attack and the sweep angle. Thereafter, an offline integrated optimization problem of both wing morphing strategy and trajectory based on the Gauss pseudo-spectral method is investigated, which can fulfill actual task requirements and variable constraints. Compared with four specific fixed-wing waverider configurations, this morphing waverider has a larger downrange distance and a lower angle of attack. However, the real flight trajectory of the morphing waverider may deviate from the nominal condition due to multiple uncertainties and this morphing waverider is required to have an intelligent online optimization ability. Therefore, an online optimization method based on the neural network is proposed for both wing morphing strategy and trajectory optimization. Finally, the overall performance of this method is examined by comparisons and Monte Carlo runs.