Fuzzy Relational Control of an Uncertain System

2014 ◽  
Vol 22 (02) ◽  
pp. 243-261 ◽  
Author(s):  
Muhammad Bilal Kadri ◽  
Arthur Dexter
Keyword(s):  
Control Strategy ◽  
Uncertain System ◽  
Relational Model ◽  
Control Performance ◽  
Fuzzy Identification ◽  
Control Activity ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Error Learning ◽  
Relational Control

The paper considers the usefulness of a control strategy based on a fuzzy relational model of the controller to counteract uncertainties caused by measurement noise and unmeasured disturbances. The fuzzy relational model is identified using a combination of feedback error learning and fuzzy identification. An important feature of the resulting fuzzy relational model is that it will generate a fuzzy output in the presence of uncertainties. Two causes of uncertainty are considered separately, the first cause of uncertainty is due to the noise on the sensor measuring the controlled variable and the second one is an unmeasured input disturbance. Results are presented that show that the fuzzy control signal is representative of the uncertainties and that conditional defuzzification can then be used to improve the control performance by reducing the control activity.

scholarly journals Feedback error learning controller for functional electrical stimulation assistance in a hybrid robotic system for reaching rehabilitation

2016 ◽  
Vol 26 (3) ◽  
Author(s):  
Francisco Resquín ◽  
Jose Gonzalez-Vargas ◽  
Jaime Ibáñez ◽  
Fernando Brunetti ◽  
José Luis Pons
Keyword(s):  
Electrical Stimulation ◽  
Functional Electrical Stimulation ◽  
Control Strategy ◽  
Motor Impairment ◽  
Research Field ◽  
Robotic System ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Error Learning ◽  
Passive Exoskeleton

Hybrid robotic systems represent a novel research field, where functional electrical stimulation (FES) is combined with a robotic device for rehabilitation of motor impairment. Under this approach, the design of robust FES controllers still remains an open challenge. In this work, we aimed at developing a learning FES controller to assist in the performance of reaching movements in a simple hybrid robotic system setting. We implemented a Feedback Error Learning (FEL) control strategy consisting of a feedback PID controller and a feedforward controller based on a neural network. A passive exoskeleton complemented the FES controller by compensating the effects of gravity. We carried out experiments with healthy subjects to validate the performance of the system. Results show that the FEL control strategy is able to adjust the FES intensity to track the desired trajectory accurately without the need of a previous mathematical model.

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
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