A predictive control economic optimiser and constraint governor based on neural models

Authors:
Maciej Ławryńczuk
Affiliations:
Institute of Control and Computation Engineering, Warsaw University of Technology, Warsaw, Poland
Venue:
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Year:
2009

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Abstract

This paper discusses a Model Predictive Control (MPC) structure for economic optimisation of nonlinear technological processes. It contains two parts: an MPC economic optimiser/constraint governor and an unconstrained MPC algorithm. Two neural models are used: a dynamic one for control and a steady-state one for economic optimisation. Both models are linearised on-line. As a result, an easy to solve on-line one quadratic programming problem is formulated. Unlike the classical multilayer control system structure, the necessity of repeating two nonlinear optimisation problems at each sampling instant is avoided.