A New MRACS Approach for Overcome Unmeasurable Disturbance Using State Variables Taking from the Model

This paper proposes a MRACS approach based on Popov hyperstability theory to address unmeasurable disturbances by utilizing state variables derived from the model.The use of this approach simplifies the system structure and reduces the number of adjustable parameters. It also addresses the issue of directly measuring the object’s state and significantly minimizes noise interference. An adjustable linear compensator is used to remove the assumption of strictly positive real. The proposed method is more effective than the current MRAC method when dealing with measured interference of known forms, demonstrating improved performance in error modeling, adaptive rate, and stability analysis.Digital simulations demonstrate the effectiveness of this approach. The output of the controlled system closely aligns with that of the reference model, even in situations with unmeasurable interference, indicating a higher application value for the method.