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Paper details
Number 1 - March 2010
Volume 20 - 2010
Nonlinear predictive control based on neural multi-models
Maciej Ławryńczuk, Piotr Tatjewski
Abstract
This paper discusses neural multi-models based on Multi Layer Perceptron (MLP) networks and a computationally efficient nonlinear Model Predictive Control (MPC) algorithm which uses such models. Thanks to the nature of the model it calculates future predictions without using previous predictions. This means that, unlike the classical Nonlinear Auto Regressive with eXternal input (NARX) model, the multi-model is not used recurrently in MPC, and the prediction error is not propagated. In order to avoid nonlinear optimisation, in the discussed suboptimal MPC algorithm the neural multi-model is linearised on-line and, as a result, the future control policy is found by solving of a quadratic programming problem.
Keywords
process control, model predictive control, neural networks, optimisation, linearisation