Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Abstract , aProportional-Derivative and Integral (PD-I) type Fuzzy-Neural Network Controller (FNNC) based on Sugeno fuzzy model is proposed for brushless DC drives to achieve satisfied performance under steady state and transient conditions. The proposed FNNC uses the speed error, change of error and the error integral as inputs. While the PD-FNNC is activated in transient states, the PI-FNNC is activated in steady state region. A transition mechanism beten the PI and PD type fuzzy-neural controllers modifies the control law adaptively. The gradient descent algorithm is used to train the FNN in direct adaptive control scheme. Presented experimental results show the effectiveness of the proposed control system, by comparing the performance of vazious control approaches including PD type FNNC, PI type FNNC and conventional PI controller, under nonlinear loads and parameter variations of the motor.
['Muammer Gökbulut', 'Beşir Dandil', 'Cafer Bal']
I am writing a research paper on a idea Development and Implementation of a Fuzzy-Neural Network Controller for Brushless DC Drives
[{"from": "human", "value": "I am writing a research paper on a idea Development and Implementation of a Fuzzy-Neural Network Controller for Brushless DC Drives"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Abstract , aProportional-Derivative and Integral (PD-I) type Fuzzy-Neural Network Controller (FNNC) based on Sugeno fuzzy model is proposed for brushless DC drives to achieve satisfied performance under steady state and transient conditions. The proposed FNNC uses the speed error, change of error and the error integral as inputs. While the PD-FNNC is activated in transient states, the PI-FNNC is activated in steady state region. A transition mechanism beten the PI and PD type fuzzy-neural controllers modifies the control law adaptively. The gradient descent algorithm is used to train the FNN in direct adaptive control scheme. Presented experimental results show the effectiveness of the proposed control system, by comparing the performance of vazious control approaches including PD type FNNC, PI type FNNC and conventional PI controller, under nonlinear loads and parameter variations of the motor."}]