Self-triggered neural tracking control for discrete-time nonlinear systems via adaptive critic learning

Abstract

In this paper, a novel self-triggered optimal tracking control method is developed based on the online action–critic technique for discrete-time nonlinear systems. First, an augmented plant is constructed by integrating the system state with the reference trajectory. This transformation redefines the optimal tracking control design as the optimal regulation issue of the reconstructed nonlinear error system. Subsequently, under the premise of ensuring the controlled system stability, a self-sampling function that depends solely on the sampling tracking error is devised, thereby determining the next triggering instant. This approach not only effectively reduces the computational burden but also eliminates the need for continuous evaluation of the triggering condition, as required in traditional event-based methods. Furthermore, the developed control method can be found to possess excellent triggering performance. The model, critic, and action neural networks are constructed to implement the online critic learning algorithm, enabling real-time adjustment of the tracking control policy to achieve optimal performance. Finally, an experimental plant with nonlinear characteristics is presented to illustrate the overall performance of the proposed online self-triggered tracking control strategy.