Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
411078 | Neurocomputing | 2010 | 17 Pages |
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.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Maciej ławryńczuk,