Neural models in computationally efficient predictive control cooperating with economic optimisation

Authors:
Maciej Ławrynczuk
Affiliations:
Institute of Control and Computation Engineering, Warsaw University of Technology, Warsaw, Poland
Venue:
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Year:
2007

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Abstract

This paper discusses the problem of cooperation of economic optimisation with Model Predictive Control (MPC) algorithms when the dynamics of disturbances is comparable with the dynamics of the process. A dynamic neural model is used in the suboptimal nonlinear MPC algorithm with Nonlinear Prediction and Linearisation (MPC-NPL), a steady-state neural model is used in approximate economic optimisation which is executed as frequently as the MPC algorithm. The MPC-NPL algorithm requires solving on-line only a quadratic programming problem whereas approximate economic optimisation needs solving a linear programming problem. As a result, the necessity of repeating two nonlinear optimisation problems at each sampling instant is avoided.