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Observer-based practical prescribed time control for fractional-order nonlinear systems with asymmetric state constraints

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Abstract

The primary emphasis of this work is on investigating the practical prescribed time tracking issue for a type of fractional-order state constrained system with immeasurable states. These unknown system states are estimated by developing a neural state observer. Then, to further address the issue of asymmetric state constraints in fractional-order systems, the improved barrier Lyapunov function is utilized throughout dynamic surface control. On this basis, the practical prescribed time control approach is presented, which not only assures that the state signals do not cross the predetermined bounds, but also that the tracking error converges to the predefined set within a prescribed time. Finally, the effectiveness and practicability of the suggested control mechanism are shown by means of two example simulations.

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Acknowledgements

This work was supported by the Fundamental Research Funds for the Central Universities and Graduate Student Innovation Fund of Donghua University of China under Grant CUSF-DH-D-2022079.

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  1. College of Information Science and Technology, Donghua University, Shanghai, 201620, People’s Republic of China

    Lu Chen, Fa Chen & Jian-an Fang

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  1. Lu Chen
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Chen, L., Chen, F. & Fang, Ja. Observer-based practical prescribed time control for fractional-order nonlinear systems with asymmetric state constraints. Neural Comput & Applic (2024). https://doi.org/10.1007/s00521-024-09801-z

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