Path-tracking of autonomous vehicles using a novel adaptive robust exponential-like-sliding-mode fuzzy type-2 neural network controller

2019 ◽  
Vol 130 ◽  
pp. 41-55 ◽  
Author(s):  
Hamid Taghavifar ◽  
Subhash Rakheja
Keyword(s):  
Neural Network ◽  
Autonomous Vehicles ◽  
Sliding Mode ◽  
Path Tracking ◽  
Neural Network Controller ◽  
Network Controller

Aviation Flight Attitude Control Technology

2014 ◽  
Vol 716-717 ◽  
pp. 1567-1571
Author(s):  
Jun Hu
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Sliding Mode ◽  
Gaussian White Noise ◽  
Training Data ◽  
External Interference ◽  
Neural Network Controller ◽  
Network Controller ◽  
Fuzzy Neural ◽  
The Impact

This paper presents a range of two miniature spacecraft attitude Fuzzy Neural Network Sliding adaptive controller, using a weighting factor to combine indirect and direct fuzzy neural network controller fuzzy neural network controller. Interval two free parameters of fuzzy neural network adaptive sliding mode controller via output feedback control law and adaptive law be adjusted online. Simulation results show that after joining 10db Gaussian white noise, and in order to reduce the impact of external interference and noise training data, a controller with respect to the range of large amount of control type II generated by the controller. Overall adaptive scheme guarantees the global stability of the closed-loop system, all signals involved are bounded in some way, and also showed a high level of tracking performance.

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