Learning event-triggered controllers for linear parameter-varying systems from data

arXiv:2506.08366v2 Announce Type: replace Abstract: Nonlinear dynamical behaviors in engineering applications can be approximated by linear-parameter varying (LPV) representations, but obtaining precise model knowledge to develop a control algorithm is difficult in practice. In this paper, we develop data-driven control strategies for event-triggered linear parameter-varying (LPV) systems with stability verifications. First, we theoretically analyze the $\theta$-persistence of excitation condition for LPV systems, which leads to feasible data-based representations. Then, in terms of the available perturbed data, we establish the stability certificates for event-triggered LPV systems with the aid of Petersen's lemma, in the sense of robust control, resulting in computationally tractable semidefinite programs whose feasible solutions yield the gain-scheduling controllers. Besides, we generalize the data-driven event-triggered LPV stabilization methods to the case of trajectory tracking, and discuss robust tracking stability accordingly. Finally, we verify the effectiveness of our theoretical derivations through numerical examples.