Observer-based Iterative and Repetitive Learning Control for a Class of Nonlinear Systems

Sheng Zhu; Xuejie Wang; Hong Liu

doi:10.1109/JAS.2017.7510463

Volume 5 Issue 5

Aug. 2018

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

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Sheng Zhu, Xuejie Wang and Hong Liu, "Observer-based Iterative and Repetitive Learning Control for a Class of Nonlinear Systems," IEEE/CAA J. Autom. Sinica, vol. 5, no. 5, pp. 990-998, Sept. 2018. doi: 10.1109/JAS.2017.7510463

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Sheng Zhu, Xuejie Wang and Hong Liu, "Observer-based Iterative and Repetitive Learning Control for a Class of Nonlinear Systems," IEEE/CAA J. Autom. Sinica, vol. 5, no. 5, pp. 990-998, Sept. 2018. doi: 10.1109/JAS.2017.7510463

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Observer-based Iterative and Repetitive Learning Control for a Class of Nonlinear Systems

doi: 10.1109/JAS.2017.7510463

Department of Information and Electrical Engineering, Zhejiang University City College, Hangzhou 310015, China

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the Third Level of Hangzhou 131 Young Talent Cultivation Plan Funding, and 2018 Soft Science Research Project of Zhejiang Provincial Science and Technology Department Zhejiang Province Construction and participate in the "The Belt and Road" Technology Innovation Community Path Research 2018C35029

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Abstract

Abstract

In this paper, both output-feedback iterative learning control (ILC) and repetitive learning control (RLC) schemes are proposed for trajectory tracking of nonlinear systems with state-dependent time-varying uncertainties. An iterative learning controller, together with a state observer and a fully-saturated learning mechanism, through Lyapunov-like synthesis, is designed to deal with time-varying parametric uncertainties. The estimations for outputs, instead of system outputs themselves, are applied to form the error equation, which helps to establish convergence of the system outputs to the desired ones. This method is then extended to repetitive learning controller design. The boundedness of all the signals in the closed-loop is guaranteed and asymptotic convergence of both the state estimation error and the tracking error is established in both cases of ILC and RLC. Numerical results are presented to verify the effectiveness of the proposed methods.
- Iterative learning control (ILC),
- observers,
- repetitive learning control (RLC),
- time-varying parametrization

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References

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Observer-based Iterative and Repetitive Learning Control for a Class of Nonlinear Systems

doi: 10.1109/JAS.2017.7510463

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