This paper demonstrates the use of multiple models in intelligent control systems where models are organised within a model space of three primitive modelling dimensions: precision, scope and generality. This approach generates a space of models to extend the operating range of control systems. Within 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 generality and scope until a satisfactory model is found. This methodology has culminated in a multi-model intelligent control system architecture that trades-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 similar situations in the future. Examples using models of a laboratory-scale process rig illustrates the adaptive reasoning and learning process of multi-model intelligent control systems. © 1999 Elsevier Science Ltd. All rights reserved.
|Number of pages||13|
|Journal||Artificial Intelligence in Engineering|
|Publication status||Published - 1999|
- Heterogeneous knowledge
- Homogeneous and heterogeneous reasoning
- Intelligent control
- Multiple models