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 co ncept of specialized learning over an operation region is considered in ord er to identify the inverse dynamics of a given plant. Some aspects concerni ng discretization and invertibility of continuous-time plants are also addr essed. In the second part of this work, a control architecture which combin es the former approach of inverse identification through specialised learni ng with a multiple model scheme is presented. Finally, simulation results a re discussed, evaluating the performance of the proposed schemes; specifica lly, the presented controllers are applied to the simulation of the control of a robot arm. Copyright (C) 1999 John Wiley & Sons, Ltd.