Artificial neural networks and approximate reasoning for intelligent control in spaceA method is introduced for learning to refine the control rules of approximate reasoning-based controllers. A reinforcement-learning technique is used in conjunction with a multi-layer neural network model of an approximate reasoning-based controller. The model learns by updating its prediction of the physical system's behavior. The model can use the control knowledge of an experienced operator and fine-tune it through the process of learning. Some of the space domains suitable for applications of the model such as rendezvous and docking, camera tracking, and tethered systems control are discussed.
Document ID
19920046541
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Berenji, Hamid R. (NASA Ames Research Center; Sterling Software, Inc. Moffett Field, CA, United States)