Dual-Loop Self-Learning Fuzzy Control for AMT Gear Engagement: Design and Experiment

2018 ◽  
Vol 26 (4) ◽  
pp. 1813-1822 ◽  
Author(s):  
Xiangyu Wang ◽  
Liang Li ◽  
Kai He ◽  
Congzhi Liu
Keyword(s):  
Fuzzy Control ◽  
Self Learning ◽  
Gear Engagement

Adaptive Neural Network Fuzzy Control Plane Double Inverted Pendulum

2013 ◽  
Vol 765-767 ◽  
pp. 2004-2007
Author(s):  
Su Ying Zhang ◽  
Ying Wang ◽  
Jie Liu ◽  
Xiao Xue Zhao
Keyword(s):  
Neural Network ◽  
Fuzzy Control ◽  
Control Theory ◽  
Inverted Pendulum ◽  
Learning Ability ◽  
Adaptive Neural Network ◽  
Pendulum System ◽  
Inverted Pendulum System ◽  
Self Learning ◽  
Controlled Object

Double inverted pendulum system is nonlinear and unstable. Fuzzy control uses some expert's experience knowledge and learns approximate reasoning algorithm. For it does not depend on the mathematical model of controlled object, it has been widely used for years. In practical engineering applications, most systems are nonlinear time-varying parameter systems. As the fuzzy control theory lacks of on-line self-learning and adaptive ability, it can not control the controlled object effectively. In order to compensate for these defects, it introduced adaptive, self-organizing, self-learning functions of neural network algorithm. We called it adaptive neural network fuzzy inference system (ANFIS). ANFIS not only takes advantage of the fuzzy control theory of abstract ability, the nonlinear processing ability, but also makes use of the autonomous learning ability of neural network, the arbitrary function approximation ability. The controller was applied to double inverted pendulum system and the simulation results showed that this method can effectively control the double inverted pendulum system.

Fuzzy Controller Design Used for Steel Length Measurement

2011 ◽  
Vol 383-390 ◽  
pp. 5176-5181
Author(s):  
Chong Sheng Hou
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Steel Plate ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Steel Plates ◽  
Control Technology ◽  
Practical Applications ◽  
Learning Function ◽  
Self Learning

The accurate measurement of the length of steel plate is the key to control technology for cross-cutting production line. Three steel plates determining-length control algorithms were deeply analyzed, in the set-length control strategy fuzzy control technology was introduced, and determining-length fuzzy controller structure and realization was analyzed; In order to improve the accuracy of steel plate crosscut, on the basis of the fuzzy control system self-learning function and artificial self-learning function were increased. Practical applications show that technical advancement, reliability of the control system and shear plate accuracy are high, fully meet the requirements of control.

Self-Learning Fuzzy Control with Temporal Knowledge for Atracurium-Induced Neuromuscular Block during Surgery

1999 ◽  
Vol 32 (3) ◽  
pp. 187-197 ◽  
Author(s):  
D.G. Mason ◽  
J.J. Ross ◽  
N.D. Edwards ◽  
D.A. Linkens ◽  
C.S. Reilly
Keyword(s):  
Fuzzy Control ◽  
Neuromuscular Block ◽  
Self Learning

scholarly journals Adaptive Fuzzy Sliding Mode Control for a Micro Gyroscope with Backstepping Controller

Micromachines ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 968 ◽  
Author(s):  
Juntao Fei ◽  
Yunmei Fang ◽  
Zhuli Yuan
Keyword(s):  
Fuzzy Control ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Learning Ability ◽  
Fuzzy Sliding Mode Control ◽  
Adaptive Fuzzy ◽  
Adaptive Fuzzy Sliding Mode ◽  
Fuzzy Sliding Mode ◽  
Self Learning ◽  
Fuzzy Parameters

This paper developed an adaptive backstepping fuzzy sliding control (ABFSC) approach for a micro gyroscope. Based on backstepping design, an adaptive fuzzy sliding mode control was proposed to adjust the fuzzy parameters with self-learning ability and reject the system nonlinearities. With the Lyapunov function analysis of error function and sliding surface function, a comprehensive controller is derived to ensure the stability of the proposed control system. The proposed fuzzy control scheme does not need to know the system model in advance and could approximate the system nonlinearities well. The adaptive fuzzy control method has self-learning ability to adjust the fuzzy parameters. Simulation studies were implemented to prove the validity of the proposed ABFSMC strategy, showing that it can adapt to the changes of external disturbance and model parameters and has a satisfactory performance in tracking and approximation.

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