Analyzing large dynamic set-point change tracking of MRAC by exploiting fuzzy logic based automatic gain tuning

A Model reference adaptive control (MRAC) belongs to the class of adaptive servo system in which the desired performance is expressed with the help of a reference model. MRAC aims to create a closed loop controller with parameters that can change the response of the system to mimic a desired response. However, analysis unravels that there is a tolerance band for the set point change which defines the effectiveness of a particular adaptive gain (γ). Any changes in the set point which is beyond this band calls for a γ-readjustment. We propose a method which aims to overcome this pitfall in conventional MRAC by fusing fuzzy logic to dynamically vary γ. In essence, a fixed γ which fails to stabilize the system response in the advent of a large change in the average value of the set point shall be empowered with fuzzy logic to do the needful. Also, this concept never requires human interference for gain adjustment. A second order Linear Time Invariant system has been considered for all illustration. The results show considerable improvement in performance over the existing conventional MRAC system.