Adaptive interval type-2 fuzzy neural network nonsingular fast terminal sliding mode control for cable-driven parallel robots

Since the cable-driven parallel robots (CDPRs) are subject to the model uncertainty and external disturbances with high nonlinearity, classic trajectory tracking control approaches cannot guarantee a finite-time and robust controlling performance. To solve this problem, the paper proposes a novel ad...

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Vydáno v:Engineering applications of artificial intelligence Ročník 136; s. 108963
Hlavní autoři: Oghabi, Emad, Kardehi Moghaddam, Reihaneh, Kobravi, Hamid Reza
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.10.2024
Témata:
Cable-driven parallel robot
Finite-time stability
Interval type-2 fuzzy systems
Neural networks
Sliding mode control
Interval type-2 fuzzy systems
Finite-time stability
Neural networks
Sliding mode control
Cable-driven parallel robot
ISSN:0952-1976
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Shrnutí:Since the cable-driven parallel robots (CDPRs) are subject to the model uncertainty and external disturbances with high nonlinearity, classic trajectory tracking control approaches cannot guarantee a finite-time and robust controlling performance. To solve this problem, the paper proposes a novel adaptive interval type-2 fuzzy neural network non-singular fast terminal sliding mode control (AIT2FNN-NFTSMC) approach, which provides a high precision finite-time robust closed-loop control system. Firstly, a non-singular fast terminal sliding manifold is introduced and then, the fast singular free control law is designed to reach the fast finite-time convergence of the CDPR states to the desired trajectories. Meanwhile, an interval type-2 fuzzy neural network (IT2FNN) system is constructed to approximate the unknown nonlinearities and the uncertainties, and the adaptive control law is realized. The finite-time stability of the closed-loop control system is proved using the Lyapunov stability theorem. Finally, numerical simulations are reported to demonstrate the effectiveness of the proposed controller method.
ISSN:0952-1976
DOI:10.1016/j.engappai.2024.108963