A Lifting Approach to Learning-Based Self-Triggered Control with Gaussian Processes

This paper investigates the design of self-triggered control for networked control systems (NCS), where the dynamics of the plant is unknown apriori. To deal with the nature of the self-triggered control, in which state measurements are transmitted to the controller a-periodically, we propose to lift the continuous-time dynamics to a novel dynamical model by taking an inter-event time as an additional input, and then, the lifted model is learned by the Gaussian processes (GP) regression. Moreover, we propose a learning-based approach, in which a self-triggered controller is learned by minimizing a cost function, such that it can take inter-sample behavior into account. By employing the lifting approach, we can utilize a gradient-based policy update as an efficient method to optimize both control and communication policies. Finally, we summarize the overall algorithm and provide a numerical simulation to illustrate the effectiveness of the proposed approach.