LONG-RANGE PREDICTION FOR POORLY-KNOWN SYSTEMS

Predictive control of the outputs or states of a system relies on the quality of the predictions over a certain time horizon into the future . The conventional ways of obtaining these predictions suffice when th e system characteristics allow either the development of a relatively accurate model or the use of a relatively short time horizon. Such is the case for many continuous processes, where the system dynamics rend er short prediction horizons adequate, or where relatively simple, pos sibly on-line adapted, low-order, input-output models yield accurate p redictions through integration. However, conventional methods cannot y ield reliable predictions for processes that are poorly modelled and r equire long prediction horizons for good control performance. Batch pr ocesses especially belong in this category, due to their high-order ti me-varying characteristics. Their changing nonlinear behaviour allows only inadequate state-space modelling from first principles, and the s ignificant long-range future effect of their current manipulated input dictates the use of long prediction horizons for satisfactory control . This paper presents a reliable strategy for long-range prediction in poorly-modelled systems. A state-space-based combination of two neura l networks in series is employed to predict system outputs and unmeasu red states over a long horizon into the future. A simulation example o f a poorly modelled, exothermic, semi-batch reaction system demonstrat es the excellent prediction capability of the new structure. The resul ts establish that the new strategy solves a realistic prediction probl em where the previously available alternatives fail.