This paper proposes a new approach for utilizing qualitative simulation techniques within a model reference adaptive system for the control of ill-defined and uncertain processes, typical of the process industries. It is argued that the practical specification of performance of industrial systems is very often imprecise and multi-valued leading to non-unique (numerical) descriptions of the reference behaviour and, further, that the lack of precise knowledge of the industrial process results in inaccurate (numerical) models of the process to be controlled. This can lead to significant deterioration in performance with respect to the desired specification necessitating empirical tuning and hence the loss of analytic properties. Qualitative simulation techniques are used to model imprecise specifications and process knowledge, and hence to generate the reference behaviour without a loss of accuracy with respect to the original specifications. The discrepancy between the actual and the reference behaviour is used to adapt a conventional control algorithm such that model-following behaviour is maintained in the face of significant disturbance to the normal behaviour. Results are presented for first- and second-order models of desired specifications. The results are very encouraging, demonstrating that accurate adaptive behaviour of ill-defined systems can be obtained without the need to corrupt, or approximate, the original specifications, and without necessitating the availability of accurate, high-order, numerical models. © 1998 Elsevier Science Ltd. All rights reserved.
|Number of pages||10|
|Journal||Engineering Applications of Artificial Intelligence|
|Publication status||Published - 1 Apr 1998|
- Model Reference Adaptive Control
- Qualitative simulation
- Uncertain dynamic systems