A Portrayal of Sliding Mode Control Through Adaptive Neuro Fuzzy Inference System with Optimization Perspective

Sliding mode control is a promising approach for designing controllers for systems with empirical characteristics. This is a favored nonlinear control strategy that effectively addresses the uncertainties present in derived mathematical models. To further enhance the stability of such systems, an Adaptive Neuro Fuzzy Inference System is employed by adapting to dynamic changes and inconsistent correlations between excitation and response. In this study, Sliding Mode Control was deployed in the feedback loop, effectively serving as a state feedback controller based on a nonlinear control law. As a two-parameter control approach, Sliding Mode Control requires careful tuning to achieve optimal performance. The integration of the Adaptive Neuro-Fuzzy System aims to bestow the two parameters of Sliding Mode Control with the ability to rapidly reduce errors to zero, thereby enhancing overall control efficiency. The research focuses on utilizing an Adaptive Neuro Fuzzy Inference System to implement Sliding Mode Control for a DC servo system while emphasizing state feedback control. The Harmony Search Optimization method is employed to optimize controller parameters effectively. The results of the research demonstrate the achievement of a best-fit value, where the minimal standard error and Best fitness are considered. This highlights the successful integration of the proposed control strategy and validates its effectiveness in providing accurate and reliable control of the real-time DC servo system.