# Data-driven Self-triggered Control via Trajectory Prediction

arXiv: https://arxiv.org/abs/2207.08596 · HTML: https://mlsanity.com/paper/2207.08596/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2022.

## Built on by (most cited first, 2 of 2)

- [Adaptive Event-triggered Reinforcement Learning Control for Complex Nonlinear Systems](https://mlsanity.com/paper/2409.19769/) (2024)
- [Adaptive Event-Triggered Policy Gradient for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2509.20338/) (2025)

## Abstract (arXiv)

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.