From Data to Stability: A Novel Approach for Controlling Unknown Linear Time-Invariant Systems with Performance Enhancement

A novel data-driven control methodology is introduced in this paper, specifically designed for unknown linear time-invariant systems. Schur stability is established through the application of Linear Matrix Inequality (LMI) conditions, and system performance is improved by leveraging the concept of D-stability. Stability and performance are ensured by incorporating LMI features, with reliance solely on a finite set of collected data, eliminating the necessity for system model identification. Hence, the original performance mapping problem undergoes a transformation into a stability issue, incorporating modified system matrices. Then, the stability condition is formulated within the framework of LMI. The effectiveness of our approach is exemplified through two specific examples, highlighting the significant and impactful results obtained. These examples serve to showcase the practical application and outcomes of our methodology within the defined scope, providing a clear demonstration of its performance and efficacy in addressing relevant scenarios.