Efficient nonlinear predictive control based on structured neural models - Ławryńczuk, Maciej (Zielona Góra: Uniwersytet Zielonogórski)
This paper describes structured neural models and a computationally efficient (suboptimal) nonlinear Model Predictive Control (MPC) algorithm based on such models. The structured neural model has the ability to make future predictions of the process without being used recursively. Thanks to the nature of the model, the prediction error is not propagated. This is particularly important in the case of noise and underparameterisation. Structured models have much better long-range prediction accuracy than the corresponding classical Nonlinear Auto Regressive with eXternal input (NARX) models.
Keywords: process control; model predictive control; neural networks; optimisation; linearisation