Learning Self-Triggered Controllers With Gaussian Processes

This article investigates the design of self-triggered controllers for networked control systems (NCSs), where the dynamics of the plant are unknown a priori. To deal with the unknown transition dynamics, we employ the Gaussian process (GP) regression in order to learn the dynamics of the plant. To design the self-triggered controller, we formulate an optimal control problem, such that the optimal control and communication policies can be jointly designed based on the GP model of the plant. Moreover, we provide an overall implementation algorithm that jointly learns the dynamics of the plant and the self-triggered controller based on a reinforcement learning framework. Finally, a numerical simulation illustrates the effectiveness of the proposed approach.

Automated summary

This article investigates the design of self-triggered controllers for networked control systems with unknown plant dynamics using Gaussian process regression. It formulates an optimal control problem to jointly design control and communication policies based on the GP model. An implementation algorithm based on reinforcement learning framework is provided for learning plant dynamics and self-triggered controller. Numerical simulation demonstrates the effectiveness of the proposed approach.