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Composite adaptive locally weighted learning control for multi-constraint nonlinear systems

Tairen Sun, Yongping Pan, Chenguang Yang

Applied Soft Computing, Volume: 61, Pages: 1098 - 1104

Swansea University Author: Chenguang Yang

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DOI (Published version): 10.1016/j.asoc.2017.09.011

Abstract

A composite adaptive locally weighted learning (LWL) control approach is proposed for a class of uncertain nonlinear systems with system constraints, including state constraints and asymmetric control saturation in this paper. The system constraints are tackled by considering the control input as an...

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Published in: Applied Soft Computing
ISSN: 1568-4946
Published: 2017
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URI: https://cronfa.swan.ac.uk/Record/cronfa35248
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Abstract: A composite adaptive locally weighted learning (LWL) control approach is proposed for a class of uncertain nonlinear systems with system constraints, including state constraints and asymmetric control saturation in this paper. The system constraints are tackled by considering the control input as an extended state variable and introducing barrier Lyapunov functions (BLFs) into the backstepping procedure. The system uncertainty is approximated by a composite adaptive LWL neural networks (NNs), where a prediction error is constructed via a series-parallel identification model, and NN weights are updated by both the tracking error and the prediction error. The update law with composite error feedback improves uncertainty approximation accuracy and trajectory tracking accuracy. The feasibility and effectiveness of the proposed approach have been demonstrated by formal proof and simulation results.
Keywords: Barrier Lyapunov function; Neural network; control saturation; state constraint; locally weighted learning
College: Faculty of Science and Engineering
Start Page: 1098
End Page: 1104

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