Fictitious Reference Signal Based Real-Time Update of State Feedback Gains and its Experimental Verification

Abstract

This paper presents a new real-time parameter tuning in the data-driven framework. We focus on the tuning of state feedback gains to realize the desired performance of closed loop systems. For a real-time update tuning of this type of a controller, the notion of fictitious reference signal or the fictitious exogenous signal is utilized to generate the optimal gains in the real-time by using the measured past data. We also explain how the optimization can be realized as a recursive computation in real-time updates. Finally, an experiment is done to verify the effectiveness of the proposed method.