Neural models in computationally efficient predictive control cooperating with economic optimisation

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 non-linear optimisation problems at each sampling instant is avoided. © Springer-Verlag Berlin Heidelberg 2007.