Adaptive tracking control of nonlinear systems with dynamic uncertainties using neural network |
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Authors: | Yu-Qun Han |
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Affiliation: | Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, and School of Automation, Southeast University , Nanjing City, China |
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Abstract: | In this paper, an adaptive neural tracking control approach is proposed for a class of nonlinear systems with dynamic uncertainties. The radial basis function neural networks (RBFNNs) are used to estimate the unknown nonlinear uncertainties, and then a novel adaptive neural scheme is developed, via backstepping technique. In the controller design, instead of using RBFNN to approximate each unknown function, we lump all unknown functions into a suitable unknown function that is approximated by only a RBFNN in each step of the backstepping. It is shown that the designed controller can guarantee that all signals in the closed-loop system are semi-globally bounded and the tracking error finally converges to a small domain around the origin. Two examples are given to demonstrate the effectiveness of the proposed control scheme. |
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Keywords: | Adaptive tracking control nonlinear systems neural network dynamic uncertainties |
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