  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.
