Integrating technology in bioelectronics into the medical field allows for the advancementof precision medicine to better care for patients. Precision medicine includes personalized treatment options that consider varying responses across patients to the same medical treatments. To better administer these medical strategies, closed-loop control is needed to make real-time changes in treatment strategies as new information is gathered. Closed-loop control of biological systems is difficult in practice due to model uncertainties and innate complexities. To cope with this issue, we need control methods that do not require full system information, can handle time-varying uncertainties, and work across various time scales. Here, we develop and apply feedback control algorithms interfaced with ion pumps and cell systems that exhibit similar challenges. A class of artificial neural networks called a Radial Basis Function (RBF) network is applied to control pH and cell migration. This approach is chosen because it can achieve real-time online control without prior knowledge of the dynamical model of the system. The controller is interfaced with an ion pump to control the pH of a solution. We then modify the update laws of the controller to handle a system where the input needs to be within a set range. This new controller is used to control cell migration. We also consider a sliding mode controller (SMC), where partial system information provides better performance and is used for the delivery of a therapeutic drug using an ion pump. To control more complex systems with constraints, we need to be able to leverage data-driven models. Thus, finally, we present a switch controller, utilizing a model predictive controller and the sliding mode controller, to reduce the control effort needed to direct cell migration in silico and, thereby, reduce off-target effects.