Observer-based multi-dimensional Taylor network decentralised adaptive tracking control of large-scale stochastic nonlinear systems

ABSTRACT

In this paper, the problem of multi-dimensional Taylor network (MTN) decentralised adaptive output-feedback tracking control is investigated for large-scale stochastic nonlinear systems. MTNs are used to approximate the unknown nonlinear functions, and a MTN state observer is designed for estimating the unmeasured states. Based on the designed MTN state observer, and by combining the backstepping technique with dynamic surface control (DSC), an adaptive MTN decentralised output-feedback tracking control scheme is developed. It is proved that the proposed control approach can guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded in probability, and the tracking errors converge to an arbitrarily small neighbourhood around the origin in the sense of mean quartic value. Finally, a numerical example is given to illustrate the effectiveness of the proposed design approach, and the simulation results demonstrate that the proposed method promises desirable real-time performance and tracking control.

KEYWORDS:

• Observer
• multi-dimensional Taylor network
• decentralised tracking control
• backstepping
• dynamic surface control

Acknowledgments

The authors would like to thank the Editor-in-Chief & Professor Eric Rogers, the anonymous reviewers and Professor Li Lu for their valuable comments for improving the paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under Grants 61673112 and 60934008, the Fundamental Research Funds for the Central Universities under Grants 2242017K10003 and 2242014K10031, and a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).