Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a novel optimal H∞ tracking-based adaptive fuzzy neural network controller (HAFNNC) for a variable speed wind turbine. The main objective of the controller is to optimise the energy captured from the wind. In the presence of large uncertainties, H∞ control approach produces oscillatory phenomenon due to the higher needed gain. In order to reduce this gain, fuzzy neural network (FNN) with online adaptation of the parameters is used to estimate the uncertain parts of the system plant and hence enable a lor gain to be used. To eliminate the trade-offs beten the H∞ tracking performance and the high gain at the control input,  have introduced a new method based on the modification of the output tracking error through the use of both the exponential function and the adaptive particle swarm optimisation (APSO) algorithm. The stability and effectiveness of the proposed method are proved by Lyapunov method and the simulations are given to demonstrate the performance of t...
['El-mahjoub Boufounas', 'Jaouad Boumhidi', 'Ismail Boumhidi']
I am writing a research paper on a idea Optimal H∞ control without reaching phase for a variable speed wind turbine based on fuzzy neural network and APSO algorithm
[{"from": "human", "value": "I am writing a research paper on a idea Optimal H\u221e control without reaching phase for a variable speed wind turbine based on fuzzy neural network and APSO algorithm"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a novel optimal H\u221e tracking-based adaptive fuzzy neural network controller (HAFNNC) for a variable speed wind turbine. The main objective of the controller is to optimise the energy captured from the wind. In the presence of large uncertainties, H\u221e control approach produces oscillatory phenomenon due to the higher needed gain. In order to reduce this gain, fuzzy neural network (FNN) with online adaptation of the parameters is used to estimate the uncertain parts of the system plant and hence enable a lor gain to be used. To eliminate the trade-offs beten the H\u221e tracking performance and the high gain at the control input,  have introduced a new method based on the modification of the output tracking error through the use of both the exponential function and the adaptive particle swarm optimisation (APSO) algorithm. The stability and effectiveness of the proposed method are proved by Lyapunov method and the simulations are given to demonstrate the performance of t..."}]