Evolving ANNs for Spacecraft Rendezvous and Docking

Abstract

We here approach the problem of designing a controller for automatic rendezvous and docking (AR&D). As a first step towards a fully reactive neurocontroller (based on state feedback), an artificial neural network is trained offline by a fitness based genetic algorithm to fulfill the docking task. Its performance using one specific docking case, with fixed initial and boundary conditions, is compared to a numerical solution of the corresponding optimal control problem. The concept of a "stop-neuron" is introduced here to the neurocontroller for the first time to co-evolve the time taken to achieve the desired docking position.