Reinforcement Learning-Based Dynamic Event-Triggered Control for Unknown Nonaffine Systems Using Dynamic Feedback

In this article, a dynamic feedback (DF)-based dynamic event-triggered (DET) control method for unknown nonaffine systems (UNSs) is developed by using reinforcement learning (RL). Through introducing a DF signal as a virtual control input, the UNS is augmented into a partially unknown affine system (PUAS). Subsequently, by designing a novel cost function that reflects the system states, and the actual and virtual control inputs, the DET optimal control (OC) problem of UNS is transformed into a DET OC problem of PUAS. To relax the requirement of PUAS dynamics, a neural network (NN)-based observer is established by using the measured system data. Moreover, a novel DET condition is established based on the static event-triggered (SET) rule, and the relationship of the triggering interval between SET and DET is revealed. In order to solve the DET Hamilton–Jacobi–Bellman equation (HJBE), a critic NN is constructed with the concurrent learning method to release the persistence of excitation (PE) condition. Furthermore, according to Lyapunov’s direct method, the stability of the closed-loop system is guaranteed under the developed DF-based DET control strategy. Finally, simulation results of two examples demonstrate the effectiveness of the present DF-based DET method.