Affiliation:
1. Research Laboratory of Numerical Control of Industrial Processes, National Engineering School of Gabes, University of Gabes, Omar Ibn Khattab Street 6029, Gabes, Tunisia
Abstract
In the present work, an indirect adaptive neural control method for nonlinear systems having unknown dynamics is proposed. The proposed control architecture is composed by a neural emulator (NE) and a neural controller (NC) where a new decoupled variable learning rates (VLRs) combined with Taylor development (TD) are used to train the NE and the NC. The developed VLRs mixed with the TD (TDVLRs) ensure a quick adaptation of neural networks parameters guaranteeing a faster output convergence and reducing the tracking error. The effectiveness of the proposed TDVLRs is illustrated by simulation with a nonlinear dynamic system. In order to validate simulation results, an application on a transesterification reactors is, also, presented.
Publisher
World Scientific Pub Co Pte Ltd
Subject
Artificial Intelligence,General Medicine
Cited by
2 articles.
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1. A novel neural emulator identification of nonlinear dynamical systems using Lyapunov stability theory;Transactions of the Institute of Measurement and Control;2023-05-16
2. Indirect Adaptive Neural Control based on VLRs of nonlinear MIMO systems;2022 IEEE 21st international Ccnference on Sciences and Techniques of Automatic Control and Computer Engineering (STA);2022-12-19