Neural-based adaptive control design for general nonlinear systems and its application to process Control

Please use this identifier to cite or link to this item: https://doi.org/10.1109/ACC.1998.694631

Title:	Neural-based adaptive control design for general nonlinear systems and its application to process Control
Authors:	Ge, S.S. Hang, C.C. Zhang, T.
Issue Date:	1998
Citation:	Ge, S.S.,Hang, C.C.,Zhang, T. (1998). Neural-based adaptive control design for general nonlinear systems and its application to process Control. Proceedings of the American Control Conference 1 : 73-77. ScholarBank@NUS Repository. https://doi.org/10.1109/ACC.1998.694631
Abstract:	In this work, a neural-based adaptive controller is presented to solve the tracking control problem for a general class of unknown nonlinear systems. The proposed controller ensures that the output tracking error converges to a small neighborhood of the origin. The weight updating law of neural networks (NNs) is derived using Lyapunov theory and the stability of the closed-loop system is guaranteed. The proposed control scheme has been successfully applied to the composition control in a continuously stirred tank reactor (CSTR) in chemical processes. © 1998 AACC.
Source Title:	Proceedings of the American Control Conference
URI:	http://scholarbank.nus.edu.sg/handle/10635/72778
ISBN:	0780345304
ISSN:	07431619
DOI:	10.1109/ACC.1998.694631
Appears in Collections:	Staff Publications

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