Research Library

open-access-imgOpen AccessRecursive Terminal Sliding-Mode Control Method for Nonlinear System Based on Double Hidden Layer Fuzzy Emotional Recurrent Neural Network
Author(s)
Chao Jia,
Deding Kong,
Lifeng Du
Publication year2022
Publication title
ieee access
Resource typeMagazines
PublisherIEEE
Aiming at the problem of the coexistence of nonlinearity and uncertainty in the control systems, a novel control method which combines the double hidden layer fuzzy emotional recurrent neural network (DHLFERNN) and the recursive terminal sliding mode control (RTSMC) is proposed. Firstly, a novel double hidden layer fuzzy emotional recurrent neural network (DHLFERNN) is designed. The proposed DHLFERNN can be considered as a combination of a fuzzy neural network (FNN) and a double hidden layer recurrent neural network (DHLRNN) in the framework of brain emotional learning (BEL), which could make the controller obtain higher nonlinear approximation ability. Secondly, a recursive terminal sliding mode control (RTSMC) method based on the DHLFERNN is proposed. In this method, the nonlinear sliding-mode equivalent control term is approximated by the proposed DHLFERNN, which makes the controller possess a good nonlinear approximation ability when the nonlinear model cannot be obtained exactly. Finally, the stability of closed-loop system is proved by the Lyapunov method, and the adaptive law of each parameter in DHLFERNN is derived. The proposed method is verified on an inverted pendulum system, and the comparison with other control methods proves that the proposed method has faster convergence speed and higher control accuracy.
Subject(s)aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Keyword(s)Biological neural networks, Brain modeling, Fuzzy neural networks, Sliding mode control, Fuzzy control, Recurrent neural networks, Emotion recognition, Brain emotional learning (BEL), double hidden layer recurrent neural network (DHLRNN), fuzzy neural network (FNN), recursive terminal sliding-mode control (RTSMC)
Language(s)English
SCImago Journal Rank0.587
H-Index127
ISSN2169-3536
DOI10.1109/access.2022.3220800

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