Dynamic event-triggered gain-scheduling control of discrete-time quasi-LPV systems

Abstract

This paper concerns the dynamic event-triggered gain-scheduling control of discrete-time nonlinear systems represented by quasi-linear parameter-varying (quasi-LPV) models. A novel dynamic triggering mechanism is proposed to cancel out the influence of asynchronous parameters induced by the event-based sampling. This new mechanism allows to derive constructive conditions to simultaneously design the gain-scheduled control law and the dynamic triggering mechanism to ensure the origin of the closed-loop system is asymptotically stable. As a sub-product, a co-design condition is readily derived to design static triggering mechanisms. Moreover, a convex optimization problem is proposed to enlarge the inter-event sampling and an estimate of the region of attraction of the closed-loop system is obtained. Numerical examples are provided to illustrate the effectiveness of the dynamic triggering strategy over its static counterpart.