Model switching in intelligent control systems

This paper demonstrates the use of multiple models in intelligent control s ystems where models are organised within a model space of three primitive m odelling dimensions: precision, scope and generality. This approach generat es a space of models to extend the operating range of control systems. With in this model space, the selection of the most appropriate model to use in a,given situation is determined through a reasoning strategy consisting of a set of model switching rules. These are based on using the most efficient , but least general models first and then incrementally increasing the gene rality and scope until a satisfactory model is found. This methodology has culminated in a multi-model intelligent control system architecture that tr ades-off efficiency with generality, an approach apparent in human problem solving. The architecture allows learning of successful adaptations through model refinement and the subsequent direct use of refined models in simila r situations in the future, Examples using models of a laboratory-scale pro cess rig illustrates the adaptive reasoning and learning process of multi-m odel intelligent control systems. (C) 1999 Elsevier Science Ltd. All rights reserved.