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- Title
Control-error-based output-feedback adaptive decentralized neural network controller for interconnected uncertain strict-feedback nonlinear systems with input saturation.
- Authors
Bey, Oussama; Chemachema, Mohamed
- Abstract
In this paper, a control-error-based decentralized neural network (NN) direct adaptive controller is presented for uncertain interconnected nonlinear systems, in strict-feedback form, subject to input saturation and external disturbances with unavailable states for measurement. Different from the existing results in the literature, the proposed approach is based on the control error instead of the tracking error resulting in a separation-like principle. Furthermore, the explosion of complexity due to back-stepping recursive design is completely avoided along with discarding all restrictive assumptions imposed on the unmatched interconnections. Actually, NNs are used to approximate the unknown ideal control laws, and auxiliary control terms are appended to deal with approximation errors and enhance the stability of the closed-loop system. Besides, fuzzy inference systems are introduced to estimate the unknown control errors, leading to simplified derivation of adaptive laws. Thanks to the strictly positive real (SPR) property, the tracking errors are proved to converge asymptotically to zero using Lyapunov theory, which is superior to bounded stability results usually found in the literature. Simulation results show the effectiveness of the proposed approach.
- Subjects
ADAPTIVE control systems; ADAPTIVE fuzzy control; NONLINEAR systems; BACKSTEPPING control method; FUZZY logic; CLOSED loop systems; PSYCHOLOGICAL feedback; APPROXIMATION error
- Publication
Transactions of the Institute of Measurement & Control, 2024, Vol 46, Issue 8, p1529
- ISSN
0142-3312
- Publication type
Article
- DOI
10.1177/01423312231198920