Control of space robots in uncertain environments using neural networks

Many space robotic systems would be required to operate in uncertain or even unknown environments. In this paper, the problem of identifying such environments for compliance control is considered. In particular, neural networks are used for identifying environments that a robot establishes contact with, with fixed nonlinear structure and unknown parameters. Compliant motion experiments have been performed with a robotic arm, where desired contact forces are computed using a neural network