On Scheduling Actuator Data Transmission in Discrete-Time Networked Adaptive Systems

We study how to schedule actuator data transmission in discrete-time networked model reference adaptive control systems for reducing wireless network utilization. We propose an event-triggered hedging approach introduced to the reference model, which is constructed by taking the difference between the sampled control signal sent to an uncertain dynamical system and the actual discrete-time control signal computed by the adaptive control algorithm. The advantages of the proposed approach include i) asymptotic convergence of the difference between an uncertain dynamical system state and the reference model state predicated on a logarithmic Lyapunov function-based stability analysis, ii) possibility of convergence of the estimated parameters to the unknown parameters when the closed-loop dynamical system is persistently excited, and iii) direct execution of the proposed approach in an embedded code. Note that i) and ii) are not trivial to achieve without introducing the proposed hedging approach to the reference model and iii) results from our discrete-time framework since existing continuous-time approaches require discretization that may result in loss of stability properties. Finally, an illustrative numerical example is provided for demonstrating the efficacy of the proposed approach.