Neural adaptive control of non-linear plants via a multiple inverse model approach

In this paper, neural architectures for controlling non-linear plants with parameter variation are proposed. In the first part of the document, the concept of specialized learning over an operation region is considered in order to identify the inverse dynamics of a given plant. Some aspects concerning discretization and invertibility of continuous-time plants are also addressed. In the second part of this work, a control architecture which combines the former approach of inverse identification through specialised learning with a multiple model scheme is presented. Finally, simulation results are discussed, evaluating the performance of the proposed schemes; specifically, the presented controllers are applied to the simulation of the control of a robot arm. Copyright (C) 1999 John Wiley & Sons, Ltd.