Computationally efficient nonlinear predictive control based on neural Wiener models

Authors:
Maciej ławryńczuk
Affiliations:
Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland
Venue:
Neurocomputing
Year:
2010

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Abstract

This paper describes a computationally efficient nonlinear model predictive control (MPC) algorithm based on neural Wiener models and its application. The model contains a linear dynamic part in series with a steady-state nonlinear part which is realised by a neural network. In the presented MPC algorithm the model is linearised on-line, as a result the future control policy is easily calculated from a quadratic programming problem. The algorithm gives control performance similar to that obtained in fully fledged nonlinear MPC, which hinges on non-convex optimisation. In order to demonstrate the accuracy and the computational efficiency of the considered MPC algorithm a polymerisation process is studied.