Fuzzy Neural Network-Based Adaptive Sliding-Mode Descriptor Observer

• Published in: IEEE transactions on fuzzy systems Vol. 32; no. 6; pp. 3342 - 3354
• Main Authors: Zhong, Zhixiong, Lam, Hak-Keung, Basin, Michael V., Zeng, Xiao-Jun
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Adaptive control; Adaptive sliding-mode descriptor observer; Adaptive systems; Algorithms; Artificial neural networks; Cerebellar model articulation controller; Compensators; Convergence; Discrete time systems; Fuzzy logic; Fuzzy sets; interval type-2 fuzzy cerebellar model articulation controller (CMAC); Linear matrix inequalities; Lithium-ion batteries; Machine learning; Mathematical models; Nonlinear systems; Observers; Rechargeable batteries; Robust control; robust control-based learning; State estimation; State observers; uncertain descriptor systems; Uncertainty
• ISSN: 1063-6706; 1941-0034
• DOI: 10.1109/TFUZZ.2024.3370693

This study examines the state estimation problem for uncertain descriptor systems subject to unknown dynamics. An integration of interval type-2 fuzzy set (IT2-FS) and cerebellar model articulation controller (CMAC) neural network, called the IT2-FCMAC approximator, is introduced to approximate the unknown dynamics and is incorporated into a sliding-mode descriptor observer. Then, its learning problem is cast into a robust control framework subject to discrete-time nonlinear systems, and a robust <inline-formula><tex-math notation="LaTeX">\mathcal {H}_{\infty }</tex-math></inline-formula> control-based learning algorithm is proposed. Besides, an adaptive compensator is introduced to mitigate the impact of approximation error. An IT2-FCMAC-based adaptive sliding-mode observer is developed and the calculation of observer gain and learning parameters is solved by several linear matrix inequalities. The proposed scheme is applied in estimating the state of charge of lithium-ion batteries, showcasing its exceptional performance.