Direct adaptive NN control of a class of nonlinear systems

Please use this identifier to cite or link to this item: https://doi.org/10.1109/72.977306

Title:	Direct adaptive NN control of a class of nonlinear systems
Authors:	Ge, S.S. Wang, C.
Keywords:	Adaptive control Backstepping Neural control Neural network (NN) Uncertain strict-feedback system
Issue Date:	Jan-2002
Citation:	Ge, S.S., Wang, C. (2002-01). Direct adaptive NN control of a class of nonlinear systems. IEEE Transactions on Neural Networks 13 (1) : 214-221. ScholarBank@NUS Repository. https://doi.org/10.1109/72.977306
Abstract:	In this paper, direct adaptive neural-network (NN) control is presented for a class of affine nonlinear systems in the strict-feedback form with unknown nonlinearities. By utilizing a special property of the affine term, the developed scheme avoids the controller singularity problem completely. All the signals in the closed loop are guaranteed to be semiglobally uniformly ultimately bounded and the output of the system is proven to converge to a small neighborhood of the desired trajectory. The control performance of the closed-loop system is guaranteed by suitably choosing the design parameters. Simulation results are presented to show the effectiveness of the approach.
Source Title:	IEEE Transactions on Neural Networks
URI:	http://scholarbank.nus.edu.sg/handle/10635/55645
ISSN:	10459227
DOI:	10.1109/72.977306
Appears in Collections:	Staff Publications

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