Suboptimal Nonlinear Predictive Control with MIMO Neural Hammerstein Models

Authors:
Maciej Ławryńczuk
Affiliations:
Institute of Control and Computation Engineering, Warsaw University of Technology, Warsaw, Poland 00-665
Venue:
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
Year:
2008

Citing 2
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Abstract

This paper describes a computationally efficient (suboptimal) nonlinear Model Predictive Control (MPC) algorithm with neural Hammerstein models. The Multi-Input Multi-Output (MIMO) dynamic model contains a steady-state nonlinear part realised by a set of neural networks in series with a linear dynamic part. The model is linearised on-line, as a result the MPC algorithm solves a quadratic programming problem. The algorithm gives control performance similar to that obtained in nonlinear MPC, which hinges on non-convex optimisation.