A Prerouted Searching Approach to Adaptive Event-Triggered Control of Uncertain Nonlinearly Parameterized Systems

This paper presents a generic design approach to the adaptive event-triggered control for a class of nonlinear systems subject to nonlinearly appearing unknown parameters and uncertain nonlinearities. The overall event-triggered control strategy relies on the co-design consisting of parameterized control laws, a parameter switching logic, and an event-triggered sampling mechanism, which guarantees that all the closed-loop states are bounded and the plant state asymptotically converges to the origin without infinitely fast sampling. Our proposed control algorithm is validated by a small-gain based systematic control design for the FitzHugh-Nagumo neural system, in which the simulation results demonstrate that a slight modification of the proposed algorithm can even cope with the system with unknown control direction.