Lateral Guidance Control of UAV Using Feedback Error Learning

2007 ◽  
Author(s):  
Akiko Matsuura ◽  
Shinji Suzuki ◽  
Mitsuru Kono ◽  
Akitoshi Sakaguchi
Keyword(s):  
Guidance Control ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Error Learning

Nonlinear Discrete-time feedback error learning with PI Controller for AC servo motor

2008 ◽  
Author(s):  
Noppanan Suwanjatuporn ◽  
Mes Napaamporn ◽  
Waree Kongprawechnon ◽  
Sirisak Wongsura
Keyword(s):  
Discrete Time ◽  
Pi Controller ◽  
Servo Motor ◽  
Ac Servo ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Error Learning ◽  
Ac Servo Motor

Feedback error learning-based type-2 fuzzy neural network predictive controller for a class of nonlinear input delay systems

2019 ◽  
Vol 41 (13) ◽  
pp. 3651-3665 ◽  
Author(s):  
Kamel Sabahi ◽  
Sehraneh Ghaemi ◽  
Mohammad Ali Badamchizadeh
Keyword(s):  
Input Delay ◽  
Time Varying ◽  
Time Varying Delay ◽  
Fuzzy Neural ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Predictive Controller ◽  
Error Learning ◽  
Varying Delay

In this paper, a type-2 fuzzy neural network predictive (T2FNNP) controller has been designed in the feedback error learning (FEL) framework for a class of input delay nonlinear systems considering of unknown disturbance and uncertainties. In this method, the predictor has been utilized to estimate the future state variables of the controlled system to compensate for the time-varying input delay. To establish the control objectives, the predicted states are fed to the controller which is in FEL framework and includes T2FNN parallel with classical feedback controller. Using the proposed T2FNNP controller, it is shown that the system output tracks the reference signal in presence of the measurement noise, time-varying delay, and bounded disturbance. An appropriate Lyapunov function has been exploited to study stability of the closed-loop system and to derive the adaptation laws for both the predictor and controller. A flexible joint robot system has been used to validate performance of the proposed T2FNNP controller and has been compared with a type-1 fuzzy sliding predictive controller through some simulations. The results indicate the efficiency of the proposed T2FNNP controller in dealing with time-varying delay as well as high level of measurement noise which exists in the sensor.

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