Controle inteligente de sistemas biológicos complexos

Abstract

Complex systems are a class of dynamic systems, typically nonlinear, characterized by the presence of multiple coupled differential equations responsible for describing the dynamic behavior of the system’s internal states. These equations cannot be analyzed in isolation as it would impair the understanding of the system’s overall behavior. Various physiobiological phenomena can be described through complex systems, especially pathologies associated with these phenomena. Controlling this class of dynamic systems allows for the development of techniques that could potentially become new medical treatments in the future. In this work, an intelligent controller is presented, whose main structure is deduced through the Lyapunov Asymptotic Stability Theorem. Embedded into this controller is an adaptive term based on Artificial Neural Networks, implemented to compensate for and predict uncertainties related to unknown model parameters, unmodeled dynamics, and external disturbances. Throughout the text, the controller is tested on different biological complex systems used to represent the brain dynamics of patients with epilepsy and the dynamics of cardiac pathologies. For each example, the controller is also tested in different scenarios where aberrant behaviors of these vital organs may occur. Numerical simulations demonstrate the effectiveness of the controller’s implementation, pointing to a viable path for the development of new treatments beyond the pharmacological area.