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Suboptimal nonlinear predictive control based on multivariable neural Hammerstein models

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Abstract

This paper describes a computationally efficient nonlinear Model Predictive Control (MPC) algorithm in which the neural Hammerstein model is used. The Multiple-Input Multiple-Output (MIMO) dynamic model contains a neural steady-state nonlinear part in series with a linear dynamic part. The model is linearized on-line, as a result the MPC algorithm requires solving a quadratic programming problem, the necessity of nonlinear optimization is avoided. A neutralization process is considered to discuss properties of neural Hammerstein models and to show advantages of the described MPC algorithm. In practice, the algorithm gives control performance similar to that obtained in nonlinear MPC, which hinges on non-convex optimization.

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Authors and Affiliations

  1. Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665, Warsaw, Poland

    Maciej Ławryńczuk

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  1. Maciej Ławryńczuk
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Correspondence to Maciej Ławryńczuk.

Additional information

This work was supported by Polish national budget funds for science for years 2009–2011 in the framework of a research project.

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Ławryńczuk, M. Suboptimal nonlinear predictive control based on multivariable neural Hammerstein models. Appl Intell 32, 173–192 (2010). https://doi.org/10.1007/s10489-010-0211-x

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