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JAXA develops an unmanned technology demonstrator vehicle for low sonic boom research. The research project is called D-SEND #2 (Drop test for Simplified Evaluation of Non-symmetrically Distributed sonic boom). The autonomous demonstrator vehicle will be dropped from a high altitude balloon, and it will accelerate and glide to fly over a predetermined measurement area with a supersonic cruise condition. Trajectory optimization analysis is necessary for the design of onboard reference trajectory generation logic, which is robust against the dropping point uncertainty. An easy-to-use trajectory optimization tool using dynamic programming approach has been developed in order to generate reference trajectories in the early stage of design process. This paper introduces how the dynamic programming approach is applied to the D-SEND #2 flight trajectory optimization, and it shows numerical examples, which include the maximum and minimum range problems, and range adjustment problems.

More and more UAVs are developed for various purposes and their flight controllers are required to have sufficient robustness and performance to realize their versatile missions. To design these sophisticated controller is pretty much time-consuming task by traditional design method. Neural network based adaptive control with dynamic inversion is applied to solve this problem. This method has two advantages. One is that the gain scheduling is not necessary because nonlinear dynamic inversion is applied to control nonlinear systems. The other is that neural network improves the controller performance by estimating parameters of the actual plant. Numerical examples show its effectiveness and its ability to adapt to modeling errors. This paper concludes that proposed method reduces the workload of controller design task and it has ability to adapt various errors of nonlinear systems.