Stable neural network control for manipulators

This paper presents a stable neural network control, scheme for manipulators. Cerebellar model articulation (CMAC) or radial basis function (RBF) neural networks are used. The main contribution of this paper is the stability proof of neural network controllers for manipulators. This distinguishes the paper from other work. The results of this paper also have a closer relation to conventional adaptive control. This means that the neural network controller can either work alone if there is no a priori knowledge or work together with conventional adaptive control. Any a priori knowledge can also easily be used to train the neural networks off-line and, therefore, improve the online performance.