Recursive Terminal Sliding-Mode Control Method for Nonlinear System Based on Double Hidden Layer Fuzzy Emotional Recurrent Neural Network

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 dou...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2022, Vol.10, p.1-1
Main Authors: Jia, Chao, Kong, DeDing, Du, Lifeng
Format: Article
Language:English
Subjects:
Approximation
Artificial neural networks
Biological neural networks
Brain emotional learning (BEL)
Brain modeling
Control methods
Control systems
Controllers
Double hidden layer recurrent neural network (DHLRNN)
Emotion recognition
Feedback control
Fuzzy control
Fuzzy logic
Fuzzy neural network (FNN)
Fuzzy neural networks
Neural networks
Nonlinear control
Nonlinear systems
Nonlinearity
Recurrent neural networks
Recursive terminal sliding-mode control (RTSMC)
Sliding mode control
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3220800