Application of Neural Network in Trajectory Planning of the Entry Vehicle for Variable Targets

A method for onboard generation of entry trajectory for variable targets is discussed. Conventional trajectory planning algorithms can only be used for the fixed terminal conditions without considering the variable targets. In case the vehicle needs to alert the entry trajectory due to damage or effectors failure, the entry guidance system must real-time design a feasible entry trajectory according to another feasible landing site from current flight conditions. The conventional approaches must be augmented to provide the real-time redesign capability for variable targets, and the redesign trajectory would also satisfy all path constraints and altered terminal conditions. This paper makes use of the neural network as a major controller to overcome this problem. The redesign trajectory problems and control parameter generations online problems can be transformed into the neural network offline training problem, given the initial conditions and the selected terminal conditions. Numerical simulations with a reusable launch vehicle model for various terminal conditions are presented to demonstrate the capability and effectiveness of the approach.