Adaptive event-triggered control of a uncertain linear discrete time system using measured input and output data

In this paper, an adaptive model-based event-triggered control of an uncertain linear discrete time system is developed. Measured input and output vectors and their history are utilized to express the unknown linear discrete-time system as an autoregressive Markov representation (ARMarkov). A novel adaptive model in the form of AR Markov is proposed and an update law is derived in order to estimate parameters of the ARMarkov model at triggered instants unlike periodic updates in standard adaptive control. Lyapunov method is used to derive the event trigger condition, prove boundedness of the parameter vector and asymptotic convergence of the outputs and states. A simulation example is utilized to verify theoretical claims and a comparison of the proposed with zero order hold (ZOH) and fixed model-based schemes is also discussed as part of simulation.