An efficient nonlinear predictive control algorithm with neural models and its application to a high-purity distillation process

Authors:
Maciej Ławryńczuk;Piotr Tatjewski
Affiliations:
Institute of Control and Computation Engineering, Warsaw University of Technology, Warsaw, Poland;Institute of Control and Computation Engineering, Warsaw University of Technology, Warsaw, Poland
Venue:
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Year:
2006

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Abstract

This paper is concerned with a computationally efficient (suboptimal) nonlinear model-based predictive control (MPC) algorithm and its application to a high-purity high-pressure ethylene-ethane distillation column. A neural model of the process is used on-line to determine the local linearisation and the nonlinear free response. In comparison with general nonlinear MPC technique, which hinges on non-convex optimisation, the presented structure is far more reliable and less computationally demanding because it results in a quadratic programming problem, whereas its closed-loop control performance is similar.