Epilepsy is one of the common neurological disorders that affects about one percent of the world’s population. Treatment of the disease is typically carried out by methods like surgery or medicine. If a patient’s condition is not suitable for surgery or he is resistance to drugs, the electrical stimulation is considered as an alternative solution which can be divided into two groups of open and closed loop. In the open loop method, an expert tries to adjust the stimulation parameters manually by notification of the current state of the patient with trial and error method. In this method, side effects of stimulation can be significant due to not considering any feedback of the system. Many computational models have been proposed to simulate epileptic seizures. Among the types of these models, Eٍpileptor model has been noticed due to features such as simplicity and producing seizures automatically. Researches have been done to offer a proper algorithm for suppressing epileptic seizures in Epileptor model, but unfortunately most of them are weak against uncertainties due to the lack of a resistant structure in the controller. In this thesis, two categories of dynamic surface and adaptive sliding mode control are presented for suppression and termination of seizures in the Epileptor and their stability is proved by using Liapanov-like functions. By adding uncertainty to the model, acute and chronic seizures are added to normal seizures and the performance of controllers in these conditions is evaluated. Next, by connecting the two Epileptors, the effectiveness of the controllers is examined against the propagation of seizure in large areas of the brain. Despite success of both controllers in reducing seizures, the simulation results show better performance of the adaptive sliding model controller in suppressing seizures at three different levels of uncertainty. closed loop electrical stimulation, Epileptor model, adaptive sliding control, dynamic surface control, state estimation