Alzheimer’s disease (AD) is a degenerative neurological condition that impacts millions of individuals across the globe and remains without a healing. In the search for new possibilities of treatments for this terrible disease, this work presents the improved Alzheimer-like  disease (IALD) model and connects it to a new control technique that establishes computationally modeled memory healing for a condition similar to AD.  The modeling proceeds  from recent etiological and pathogenetic hypotheses of AD related to amyloid precursor protein (APP). For the IALD model, a continuous Hopfield neural network (HNN) with delay is used. In the control, techniques from the area of robust control are used, which is based on new discoveries in Lurie control systems. In addition, this paper reviews the development of the Alzheimer-Like Disease (ALD) model, as well as, the relationship of Hopfield’s network with Lurie system. Simulations are executed to validate the model and to show the efficacy of applying a new theorem from Lurie’s problem. With the results presented, this work puts to good use, from the \textit{in silico} IALD model, the possibility of developing microcontrollers for future \textit{in vivo} experiments using a control system capable of mitigating the effect of memory loss arising from AD.