Prescribed Performance Neural Control of Strict-Feedback Systems via Disturbance Observers

Joint Authors

Yang, Chunzhi
Zhu, Fang
Xu, Guangkui
Xiang, Wei

Source

Complexity

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-10-29

Country of Publication

Egypt

No. of Pages

12

Main Subjects

Philosophy

Abstract EN

This paper provides a disturbance observer-based prescribed performance control method for uncertain strict-feedback systems.

To guarantee that the tracking error meets a design prescribed performance boundary (PPB) condition, an improved prescribed performance function is introduced.

And radial basis function neural networks (RBFNNs) are used to approximate nonlinear functions, while second-order filters are employed to eliminate the “explosion-complexity” problem inherent in the existing method.

Meanwhile, disturbance observers are constructed to estimate the compounded disturbance which includes time-varying disturbances and network construction errors.

The stability of the whole closed-loop system is guaranteed via Lyapunov theory.

Finally, comparative simulation results confirm that the proposed control method can achieve better tracking performance.

American Psychological Association (APA)

Xiang, Wei& Xu, Guangkui& Zhu, Fang& Yang, Chunzhi. 2020. Prescribed Performance Neural Control of Strict-Feedback Systems via Disturbance Observers. Complexity،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1144772

Modern Language Association (MLA)

Xiang, Wei…[et al.]. Prescribed Performance Neural Control of Strict-Feedback Systems via Disturbance Observers. Complexity No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1144772

American Medical Association (AMA)

Xiang, Wei& Xu, Guangkui& Zhu, Fang& Yang, Chunzhi. Prescribed Performance Neural Control of Strict-Feedback Systems via Disturbance Observers. Complexity. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1144772

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1144772