LAUSR

For nonlinear systems, a novel adaptive dynamic programming (ADP) algorithm of self-triggered control (STC) strategy is proposed. This is a novel attempt to introduce self-triggering into the ADP algorithm. First, an identifier based on a generalized fuzzy hyperbolic model (GFHM) is established, which only uses input–output data to reconstruct the unknown system, thus reducing the requirements for system dynamics. Then, the critic neural network (NN) adjusts continuously, while actor NN updates the control strategy only at triggering instants. The event-triggered control (ETC) reduces the use of control resources and improves the anti-interference capability. However, it requires dedicated hardware to monitor whether triggering rules are violated, which is not feasible on most general-purpose devices. Hence, we propose a novel technique, which uses the current state of the device to determine the state measurement at the next moment, calculate the control law, and then abandon persistently monitoring of the plant. This technique is called STC. Finally, the closed-loop system is guaranteed to be ultimate uniform boundedness (UUBs). Furthermore, a simulation example is given.