Data-driven Self-triggered Control via Trajectory Prediction

TL;DR

This paper introduces data-driven self-triggered control methods for unknown linear systems, utilizing offline data to design controllers and triggering laws that reduce communication while ensuring stability and performance.

Contribution

It develops novel data-driven self-triggered control schemes for unknown linear systems, including output feedback and state feedback approaches, with proven stability.

Findings

Feasibility of the proposed control schemes is established.

Stability of the controllers is proven.

Numerical examples validate effectiveness.

Abstract

Self-triggered control, a well-documented technique for reducing the communication overhead while ensuring desired system performance, is gaining increasing popularity. However, existing methods for self-triggered control require explicit system models that are assumed perfectly known a priori. An end-to-end control paradigm known as data-driven control learns control laws directly from data, and offers a competing alternative to the routine system identification-then-control method. In this context, the present paper puts forth data-driven self-triggered control schemes for unknown linear systems using data collected offline. Specifically, for output feedback control systems, a data-driven model predictive control (MPC) scheme is proposed, which computes a sequence of control inputs while generating a predicted system trajectory. A data-driven self-triggering law is designed using the predicted trajectory, to determine the next triggering time once a new measurement becomes available. For state feedback control systems, instead of capitalizing on MPC to predict the trajectory, a data-fitting problem using the pre-collected input-state data is solved, whose solution is employed to construct the self-triggering mechanism. Both feasibility and stability are established for the proposed self-triggered controllers, which are validated using numerical examples.