Neural network based motion control and applications to non-holonomic mobile manipulators

This paper presents a neural network based motion control approach and its application to a mobile manipulator. The mobile base is subject to a nonholonomic constraint and the base and onboard manipulator cause disturbances to each other. Compensational neural network controllers are proposed to track dynamic trajectories under a non-holonomic constraint and uncertainties. Comparisons were made between neural network controllers with and without model information. It is shown through various simulations and application results that the proposed neural network compensation schemes can achieve good control performances.