Event-Triggered Direct Data-Driven Iterative Learning Control for Multiagent Systems

Aiming to solve issues of limited resources in topology network communication, unavailability of the mathematical models, direct controller design without considering system dynamical formulation, and lack of efficient use of learning ability from repetitive operations, an event-triggered direct data driven iterative learning control (ET-DirDDILC) is developed for a multiagent system (MAS). Since the control protocol directly affects control performance, there is definitely a close relationship between the consensus performance of the agents and the control protocols. To this end, a nonaffine nonlinear relationship of consensus error regarding the control protocol is established. Then, to deal with the unknown nonlinearity, a dynamic linear input-output relationship between two triggered batches is established by an event-triggering linearly parametric data model (ET-LPDM) where a triggering mechanism is designed along the iteration axis. Furthermore, both the event-triggered control law and the event-triggered parameter estimation law are derived from two objective functions, respectively, by using the ET-LPDM, where the values at nontriggering iteration remain unchanged from the latest triggering iteration to reduce the consumption of system resources. The proposed ET-DirDDILC does not rely on the MAS dynamical formulation. The convergence is proved and simulation study verifies the effectiveness of the presented ET-DirDDILC for MASs with both fixed and switching topologies.