Optimization of Interplanetary Rendezvous Trajectories for Solar Sailcraft Using a Neurocontroller

As for all low-thrust spacecraft, finding optimal solar sailcraft trajectories is a difficult and time-consuming task that involves a lot of experience and expert knowledge, since the convergence behavior of optimizers that are based on numerical optimal control methods depends strongly on an adequate initial guess, which is often hard to find. Even if the op-timizer converges to an ”optimal trajectory”, this trajectory is typically close to the initial guess that is rarely close to the global optimum. This paper demonstrates, that artificial neural networks in combination with evolutionary algorithms can be applied successfully for optimal solar sailcraft steering. Since these evolutionary neurocontrollers explore the trajectory search space more exhaustively than a human expert can do by using tradi-tional optimal control methods, they are able to find steering strategies that generate better trajectories, which are closer to the global optimum. Results are presented for a Near Earth Asteroid rendezvous mission and for a Mercury rendezvous mission

Training Reinforcement Neurocontrollers Using The Polytope Algorithm

A new training algorithm is presented for delayed reinforcement learning problems that does not assume the existence of a critic model and employs the polytope optimization algorithm to adjust the weights of the action network so that a simple direct measure of the training performance is maximized. Experimental results from the application of the method to the pole balancing problem indicate improved training performance compared with critic-based and genetic reinforcement approaches. Keywords: reinforcement learning, neurocontrol, optimization, polytope algorithm, pole balancing, genetic reinforcement. TRAINING REINFORCEMENT NEUROCONTROLLERS USING THE POLYTOPE ALGORITHM Abstract A new training algorithm is presented for delayed reinforcement learning problems that does not assume the existence of a critic model and employs the polytope optimization algorithm to adjust the weights of the action network so that a simple direct measure of the training performance is maximized. Exper..