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- W2790509577 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." @default.
- W2790509577 created "2018-03-29" @default.
- W2790509577 creator A5026467999 @default.
- W2790509577 date "2018-03-26" @default.
- W2790509577 modified "2023-09-23" @default.
- W2790509577 title "Adaptive tracking control of nonlinear systems with dynamic uncertainties using neural network" @default.
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- W2790509577 doi "https://doi.org/10.1080/00207721.2018.1453955" @default.
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