Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  present a design method for a discrete-time iterative learning control system by using output recurrent fuzzy neural network (ORFNN). Two ORFNNs are employed to design the control structure. One is used as an identifier called output recurrent fuzzy neural identifier (ORFNI) and the other used as a controller called output recurrent fuzzy neural controller (ORFNC). The ORFNI for identification of the unknown plant is introduced to provide the plant sensitivity which is then applied to the design of ORFNC. All the ights of ORFNI and ORFNC will be tuned during the control iteration and identification process respectively in order to achieve a desired learning performance. The adaptive laws for the ights of ORFNI and ORFNC and the analysis of learning performances are determined via a Lyapunov like analysis. It is shown that the identification error will asymptotically converge to zero and output tracking error will asymptotically converge to a residual set which depends on the initial resetting error.
['Ying-Chung Wang', 'Chiang-Ju Chien', 'Der-Tsai Lee']
I am writing a research paper on a idea An output recurrent fuzzy neural network based iterative learning control for nonlinear systems
[{"from": "human", "value": "I am writing a research paper on a idea An output recurrent fuzzy neural network based iterative learning control for nonlinear systems"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  present a design method for a discrete-time iterative learning control system by using output recurrent fuzzy neural network (ORFNN). Two ORFNNs are employed to design the control structure. One is used as an identifier called output recurrent fuzzy neural identifier (ORFNI) and the other used as a controller called output recurrent fuzzy neural controller (ORFNC). The ORFNI for identification of the unknown plant is introduced to provide the plant sensitivity which is then applied to the design of ORFNC. All the ights of ORFNI and ORFNC will be tuned during the control iteration and identification process respectively in order to achieve a desired learning performance. The adaptive laws for the ights of ORFNI and ORFNC and the analysis of learning performances are determined via a Lyapunov like analysis. It is shown that the identification error will asymptotically converge to zero and output tracking error will asymptotically converge to a residual set which depends on the initial resetting error."}]