Event-triggered intelligent critic control with input constraints applied to a nonlinear aeroelastic system

In this paper, we establish an event-triggered intelligent control scheme with a single critic network, to cope the optimal stabilization problem of nonlinear aeroelastic systems. The main contribution lies in design novel triggering condition input constraints, avoiding Lipschitz assumption on inverse hyperbolic tangent function. Based improved weight updating criterion that eliminates requirement initial admissible control, law is obtained approximately by online training network. Lyapunov stability and Zeno phenomenon closed-loop system are analysed. feasibility established algorithm verified applying it task system. results reveal developed approach can handle input-constrained problems, performance comparable time-based method updates inputs at each instant, while reducing computational communication's load.