Electro-optical sighting and tracking systems (EOSTS) are designed for accurate target sighting and tracking within any environment. For various types of electro-optical sighting devices, either an active or passive balance system is used. However, passive systems for vision devices represent multi-input and multi-output system, non-linear in nature and contains a strong correlation between the changing states of the system. Therefore, it represents a complex system that is difficult to control. The study presents an analysis of the visual field stabilized systems for vision devices, along with finding a nonlinear and linear computational model for the passive system. It proposes also a method for controlling the passive visual field stabilized system for vision devices. To avoid the coupling among the system states, a fuzzy controller was designed and verified for a EOSTS computational model. The controller represents a dynamic learning of adaptation rules that is less dependent on the designer information about the plant, which may be difficult to acquire. The controller contains a learning mechanism capable of modifying the controller’s information base by monitoring the system’s performance during operation. The learning mechanism is based on a reference model that describes the performance rates to be achieved.