Hedging Approach for Scheduling Actuator Data Transmission in Networked Adaptive Control Systems ?

摘要

In the execution of networked control systems, a critical task is to reduce wireless network utilization while achieving closed-loop stability in the presence of system uncertainties. Motivated by this standpoint, we propose a new event-triggered model reference adaptive control architecture predicated on the hedging approach to schedule the actuator data transmission from the proposed adaptive control law to the uncertain dynamical system. The hedging approach alters the trajectories of an ideal reference model to allow for correct adaptation while reducing the actuator data transmission. Specifically, we show that the difference between the trajectories of the uncertain dynamical system and the hedged reference model vanishes asymptotically using Lyapunov stability theory. Unlike existing event-triggered adaptive control methods addressing the same problem, we show with the proposed architecture, in a non-conservative way, how the trajectories of the uncertain dynamical system eventually stays close to a given ideal reference model, and it can also allow for parameter convergence when the closed-loop system is persistently excited. An illustrative numerical example is also provided to demonstrate the efficacy of the proposed architecture over existing event-triggered adaptive control methods.