Computationally efficient nonlinear predictive control based on state-space neural models

Authors:
Maciej Ławryńczuk
Affiliations:
Institute of Control and Computation Engineering, Warsaw University of Technology, Warsaw, Poland
Venue:
PPAM'09 Proceedings of the 8th international conference on Parallel processing and applied mathematics: Part I
Year:
2009

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Abstract

This paper describes a computationally efficient nonlinear Model Predictive Control (MPC) algorithm in which a state-space neural model of the process is used on-line. The model consists of two Multi Layer Perceptron (MLP) neural networks. It is successively linearised on-line around the current operating point, as a result the future control policy is calculated by means of a quadratic programming problem. The algorithm gives control performance similar to that obtained in nonlinear MPC, which hinges on non-convex optimisation.