Adaptive recurrent neural control for robot trajectory tracking including friction

The paper extends the results previously obtained for trajectory tracking of unknown plants using recurrent neural networks. The proposed controller structure, which consider systems with less inputs than states, is composed of a neural identifier and a control law defined by using the inverse optimal control approach. This new control scheme is applied to a robotic manipulator model, which includes friction terms.