Infinite-Mode Networks for Motion Control

In this paper, a novel multiple-input-multiple-output network model entitled "infinite-mode networks" (IMNs) is explained. The model proposes a new and challenging design concept. It is a dual structure and combines neural networks (NNs) to linear models. It has mathematically clear input-output relationship as compared to NNs. The model has a desired embedded internal function, which roughly determines a route for the whole system to follow as DNA does for biological systems. By this model, infinitely many error dimensions can be defined, and each error converges to zero in a stable manner. The network outputs include logical combinations of infinite modes of reference states, which consequently result in a substantial improvement of the control system performance. In order to support the network theory, time-delay and noise-suppression experiments on a four-channel haptic bilateral teleoperation control system are analyzed. An analysis between NNs, sliding-mode NNs, and IMNs is introduced. Possible future applications of IMNs are discussed.