A new neural networks based adaptive model predictive control for unknown multiple variable non-linear systems Online publication date: Tue, 16-Dec-2008
by Daohang Sha
International Journal of Advanced Mechatronic Systems (IJAMECHS), Vol. 1, No. 2, 2008
Abstract: In this paper, a novel adaptive model predictive control (AMPC) based on neural networks for unknown MIMO non-linear systems was proposed. Firstly, a new recursive second order online learning algorithm with a forgetting factor was developed for the training of the neural network model which is used to identify the unknown non-linear system. Secondly, a new cost function based on the system output error and control inputs with a forgetting factor was adopted. Thus, a new adaptive MPC control law was obtained based on the extreme value theory. The effectiveness of the proposed algorithm was demonstrated by simulation experiments to a two-input and two-output non-linear system. The algorithms both for identification and control were implemented in SIMULINK, which are ready for the real-time control. The results achieved confirmed the validity and suitability of the algorithms proposed in the paper.
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