State identification for a class of uncertain switched systems by differential neural networks

ABSTRACT

This paper presents a non-parametric identification scheme for a class of uncertain switched nonlinear systems based on continuous-time neural networks. This scheme is based on a continuous neural network identifier. This adaptive identifier guaranteed the convergence of the identification errors to a small vicinity of the origin. The convergence of the identification error was determined by the Lyapunov theory supported by a practical stability variation for switched systems. The same stability analysis generated the learning laws that adjust the identifier structure. The upper bound of the convergence region was characterized in terms of uncertainties and noises affecting the switched system. A second finite-time convergence learning law was also developed to describe an alternative way of forcing the identification error’s stability. The study presented in this paper described a formal technique for analysing the application of adaptive identifiers based on continuous neural networks for uncertain switched systems. The identifier was tested for two basic problems: a simple mechanical system and a switched representation of the human gait model. In both cases, accurate results for the identification problem were achieved.

KEYWORDS:

• Hybrid systems
• switched systems
• practical stability
• differential neural networks
• adaptive identification

Data availability statement

Data available on request due to privacy/ethical restrictions

Disclosure statement

No potential conflict of interest was reported by the author(s).

Acknowledgments

This document is the results of the research project funded by the Tecnologico de Monterrey, Institute of Advanced Materials for Sustainable Manufacturing under the grant Challenge-Based Research Funding Program 2022 number I006-IAMSM004-C4-T2-T.

Additional information

Funding

This document is the results of the research project funded by the Tecnologico de Monterrey, Institute of Advanced Materials for Sustainable Manufacturing under the grant Challenge-Based Research Funding Program 2022 number I006-IAMSM004-C4-T2-T.