Data-Driven Control for Multi-Input Systems With Partly Unknown Models Under Aperiodic Sampling

This article investigates the data-driven control problem of linear time-invariant (LTI) multi-input systems under aperiodic sampling, where the input of the system is determined by the corresponding agent. Focusing on partially unknown system models, we provide a methodology that integrates Integral Quadratic Constraints (IQC) with dissipation inequalities to derive the asymptotic stability criterion and controller design method for such systems. Firstly, addressing the challenge of incomplete system model knowledge, this article develops adaptive system identification algorithm for the above two scenarios, respectively. Each algorithm facilitates accurate model reconstruction directly from the available data. Then, the feedback interconnection between the LTI multi-input system and time-delay operator is established, and the asymptotic stability criterion for the system is demonstrated. In addition, by means of matrix decoupling techniques, a restricted frequency-domain data-driven framework which can effectively address complex coupling dynamics and overcome structural perturbation constraints is proposed, along with two corresponding controller algorithms. Finally, through the simulation analysis and result verification of numerical examples, the effectiveness of the proposed methods are demonstrated.