Predictive control of a distillation column using a control-oriented neural model

Authors:
Maciej Ławryńczuk
Affiliations:
Institute of Control and Computation Engineering, Warsaw University of Technology, Warsaw, Poland
Venue:
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
Year:
2011

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Abstract

This paper describes a special neural model developed with the specific aim of being used in nonlinear Model Predictive Control (MPC). The model consists of two neural networks. The model structure strictly mirrors its role in a suboptimal (linearisation-based) MPC algorithm: the first network is used to calculate on-line the influence of the past, the second network directly estimates the time-varying stepresponse of the locally linearised neural model, without explicit on-line linearisation. Advantages of MPC based on the described model structure (high control accuracy, computational efficiency and easiness of development) are demonstrated in the control system of a distillation column.