Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/81978
Title: Adaptive Neural PD Control With Semiglobal Asymptotic Stabilization Guarantee
Authors: Pan, Yongping
Yu, Haoyong
Er, Meng Joo
Keywords: Adaptive approximation
Asymptotic stabilization
Proportional-derivative (PD) control
Radial-basis-function neural network
Semiglobal stability
Uncertain nonlinear system
Issue Date: 2014
Source: Pan, Y., Yu, H., & Er, M. J. (2014). Adaptive Neural PD Control With Semiglobal Asymptotic Stabilization Guarantee. IEEE Transactions on Neural Networks and Learning Systems, 25(12), 2264-2274.
Series/Report no.: IEEE Transactions on Neural Networks and Learning Systems
Abstract: This paper proves that adaptive neural plus proportional-derivative (PD) control can lead to semiglobal asymptotic stabilization rather than uniform ultimate boundedness for a class of uncertain affine nonlinear systems. An integral Lyapunov function-based ideal control law is introduced to avoid the control singularity problem. A variable-gain PD control term without the knowledge of plant bounds is presented to semiglobally stabilize the closed-loop system. Based on a linearly parameterized raised-cosine radial basis function neural network, a key property of optimal approximation is exploited to facilitate stability analysis. It is proved that the closed-loop system achieves semiglobal asymptotic stability by the appropriate choice of control parameters. Compared with previous adaptive approximation-based semiglobal or asymptotic stabilization approaches, our approach not only significantly simplifies control design, but also relaxes constraint conditions on the plant. Two illustrative examples have been provided to verify the theoretical results.
URI: https://hdl.handle.net/10356/81978
http://hdl.handle.net/10220/41049
ISSN: 2162-237X
DOI: 10.1109/TNNLS.2014.2308571
Rights: © 2014 IEEE.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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