Application of Reservoir Computing to Trajectory Control Laws Using Neural ODEs for Continuous System Deep Learning

Trajectory control laws based on neural ordinary differential equations (ODEs) were proposed by the authors in a previous study. In the present study, the trajectory control laws are extended by applying the reservoir computing framework. The parameters that correspond to the intermediate layer of the trajectory control laws using neural ODEs are excluded from the decision variables. This can significantly reduce the number of parameters to be optimized hence the computational cost for training while maintaining the structure of the control laws. This enhancement will allow for use of general-purpose nonlinear optimization algorithms, extending the application of neural ODEs to mission design optimization beyond design of control laws.