Double event-triggered based anti-disturbance optimal control for nonlinear systems using adaptive dynamic programming

This article presents a robust anti-disturbance optimal control approach for nonlinear systems with different disturbances, utilizing adaptive dynamic programming (ADP) techniques. Through the integration of zero-sum (ZS) game and nonlinear disturbance observer (NDO), we not only address the challenge of controlling multiple disturbances, but also achieve precise anti-disturbance results. The problem of dealing with mismatched disturbances is formulated as a ZS game, and then the optimal control strategy under the worst-case disturbances is derived within the framework of ADP. Meanwhile, the NDO is specifically tailored to estimate the dynamics of those disturbances occurring at input port. Furthermore, a novel double event-triggered mechanism has been developed to alleviate the burden of actuators resulting from state transfer and disturbance estimation. Its unique feature is based on the principle of first-arrival and same-trigger while ensuring synchronization of signal transmission. Depending on the estimated disturbances and the designed weight update law with critic neural network, a control input is generated to guarantee that the closed-loop systems are asymptotically stable. Numerous simulations have been conducted to validate the efficacy of the proposed algorithm in terms of conserving bandwidth resources and anti-disturbance control. The suitability for practical application in autonomous underwater vehicles (AUVs) has further been affirmed through the Gazebo AUV simulator.