TY - JOUR
T1 - State identification for a class of uncertain switched systems by differential neural networks
AU - Chairez, Isaac
AU - Garcia-Gonzalez, Alejandro
AU - Luviano-Juarez, Alberto
N1 - Publisher Copyright:
© 2024 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - adaptive identification
KW - differential neural networks
KW - Hybrid systems
KW - practical stability
KW - switched systems
UR - http://www.scopus.com/inward/record.url?scp=85182168777&partnerID=8YFLogxK
U2 - 10.1080/0954898X.2023.2296115
DO - 10.1080/0954898X.2023.2296115
M3 - Artículo
C2 - 38205951
AN - SCOPUS:85182168777
SN - 0954-898X
JO - Network: Computation in Neural Systems
JF - Network: Computation in Neural Systems
ER -