Özet:

The unmodelled dynamics and disturbing effects of real-time systems prevent the system from working properly. The controls designed to control the system must take into account the unwanted effects. In this study, an adapted uncertainty model-based model-circular controller (UMPC) is recommended for linear systems. Modelleyicide uses the structure of the artificial nervous network (YSA) to ensure the rapid approach of the uncertainty function with an adapted learning step. Adjusted uncertainty model-based model-circular controller’s stability is shown with Lyapunov’s candidate function. The standard MPC and the recommended UMPC are applied to real-time DC/DC power converter control. When standard MPC is used, unknown parameters and the DC/DC converter caused by environmental noise have not provided good monitoring. However, with the implementation of the proposed structure, uncertainty was predicted and the impact in the system dynamics was used to obtain accurate and successful monitoring results. The recommended structure is expected to be used in subsequent studies.

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