Paper Title
On The Fuzzy-Neural Adaptive Iterative Learning Control for Alzheimer’s Disease

Abstract
In this paper, we propose the Alzheimer’s disease therapy which is realized by the adaptive iterative learning control (AILC) for uncertain nonaffine nonlinear Alzheimer’s systems with disturbances induced by reactive oxygen species.By using a mixed time-domain and s-domain technique, we derive a relative degree one output tracking error model with some unknown nonlinearities to solve the relative degree problem. Without using differentiators, some auxiliary signals and an averaging filter are also introduced to design the proposed AILC. Based on the output tracking error model,the information of state vector and control input is applied as the input of the filtered fuzzy neural network forcompensating of the unknown nonlinearities of Alzheimer’s systems. In order to overcome the lumped uncertainties induced by function approximation error and disturbances induced by reactive oxygen species, a normalization signal is applied as a bounding function for designing the robust ILC in the AILC. Besides, the stabilized ILC in the AILC is used to guarantee the boundedness of internal signals. By using a Lyapunov like analysis, we show that all the adjustable parameters as well as internal signals remain bounded for all iterations.The norm of output tracking error will asymptotically converge to a tunable residual setwhose size depends on some design parameters of averaging filter. Finally, an example is givento verify the nice tracking performance of the proposed AILC scheme. Keywords - Alzheimer’s Disease Therapy, Alzheimer’s Disease Systems, Adaptive Iterative Learning Control, Disturbances, Reactive Oxygen Species.