Stable neural network control for manipulators
Yichuang Jin, Tony Pipe, Alan F. T. Winfield · Intelligent Systems Engineering · 1993
The 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 the paper is a stability proof for neural networks in manipulator control. This distinguishes the paper from other work where no such proofs are given. The results of the 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 be easily used to train the neural networks off-line and, therefore, improve the on-line performance.