Data-Driven H ∞ Performance Analysis for Event-Triggered Model-Free Systems: A Data-Based Expression Method

This paper investigates the data-driven H∞performance analysis for event-triggered model-free continuous-time systems by using a data-based expression method. Here, the data-based expression method means that the closed-loop dynamics can be represented by noisy data sequences. First, a data information equation is formulated, where the unknown system matrices can be regarded as its solution. Combined with Lyapunov theory, the data-driven stability conditions and data-driven Zeno-free behavior can be achieved for event-triggered model-free continuous-time systems from noisy data sequences. Also, the derived data-driven conditions can be expressed by both LMIs- (linear matrix inequalities) and algebraic- forms. In contrast with the existing data-driven techniques, the advantages of the proposed method are that the data-driven conditions can be directly derived without establishing pre-appointed form/sizes/structure model-based LMIs conditions in advance. Finally, the results of data-driven method and an application to IEEE 39-bus test system are well discussed and analyzed in contrast with the model-known ideal case.