Online Self-Triggered Transmission Control With Critic Learning for Discrete Nonlinear Systems

In this article, a novel online self-triggered transmission control (STTC) framework is constructed based on the critic learning technique, which aims at tackling the optimal regulation issue of discrete-time nonlinear systems. On the premise of ensuring the system stability, a self-sampling function is designed only related to the sampling state, so that the next triggering moment can be determined. This not only effectively reduces the computational burden, but also avoids continuous judgment for the triggering condition similar to traditional event-based methods. Furthermore, the developed control method can be found to possess excellent triggering performance through theoretical analysis. Then, the model, critic, and action networks are established to execute the online critic learning algorithm, which make the control policy is adjusted in real-time to the optimal level. Finally, an experimental plant with nonlinear characteristics is given to illustrate the overall performance of the proposed online STTC method.