Force control of a Scara robot using neural networks

This paper describes experimental results achieved by applying artificial neural networks (NNs) in trying to perform force/position control of a real Scara manipulator robot. The general control strategy consists of a neural controller that operates in parallel with a conventional controller based on feedback error learning architecture. Two NNs are used: one for the position loop control and another to the force loop control based on the structure known as the hybrid force/position control. The main purpose was to let the NNs compensate the dynamical effects that arises when a manipulator is in contact with an environment. Successful results have been achieved for the position loop control but practical problems were observed which are related to the force loop control using NNs even with different sets of input data for this NN. Practical problems are discussed.