# Event-triggered Control and Online Learning for Networked Systems under Computational Delays

arXiv: https://arxiv.org/abs/2608.29576 · HTML: https://mlsanity.com/paper/2608.29576/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 46 references that are in the map.
- First posted on arXiv 30 Aug 2026.

## Builds on (most cited first, 20 of 20)

- [When Gaussian Process Meets Big Data: A Review of Scalable GPs](https://mlsanity.com/paper/1807.01065/) (2018)
- [Online Learning: A Comprehensive Survey](https://mlsanity.com/paper/1802.02871/) (2018)
- [Trading Inference-Time Compute for Adversarial Robustness](https://mlsanity.com/paper/2501.18841/) (2025)
- [A Modular Robotic Arm Control Stack for Research: Franka-Interface and FrankaPy](https://mlsanity.com/paper/2011.02398/) (2020)
- [Feedback Linearization based on Gaussian Processes with event-triggered Online Learning](https://mlsanity.com/paper/1911.06565/) (2019)
- [Learning-based Symbolic Abstractions for Nonlinear Control Systems](https://mlsanity.com/paper/2004.01879/) (2020)
- [Safe exploration in reproducing kernel Hilbert spaces](https://mlsanity.com/paper/2503.10352/) (2025)
- [Event-triggered Learning](https://mlsanity.com/paper/1904.03042/) (2019)
- [Can Learning Deteriorate Control? Analyzing Computational Delays in Gaussian Process-Based Event-Triggered Online Learning](https://mlsanity.com/paper/2305.08169/) (2023)
- [PACSBO: Probably approximately correct safe Bayesian optimization](https://mlsanity.com/paper/2409.01163/) (2024)
- [Towards safe and tractable Gaussian process-based MPC: Efficient sampling within a sequential quadratic programming framework](https://mlsanity.com/paper/2409.08616/) (2024)
- [How Training Data Impacts Performance in Learning-based Control](https://mlsanity.com/paper/2005.12062/) (2020)
- [Multi-Rate Control Design Leveraging Control Barrier Functions and Model Predictive Control Policies](https://mlsanity.com/paper/2004.01761/) (2020)
- [Streaming Generated Gaussian Process Experts for Online Learning and Control: Extended Version](https://mlsanity.com/paper/2508.03679/) (2025)
- [Learning-based Control for PMSM Using Distributed Gaussian Processes with Optimal Aggregation Strategy](https://mlsanity.com/paper/2307.13945/) (2023)
- [Model Identification and Adaptive State Observation for a Class of Nonlinear Systems](https://mlsanity.com/paper/2010.05251/) (2020)
- [Learning-based Prescribed-Time Safety for Control of Unknown Systems with Control Barrier Functions](https://mlsanity.com/paper/2403.08054/) (2024)
- [Learning-based Parameterized Barrier Function for Safety-Critical Control of Unknown Systems](https://mlsanity.com/paper/2408.05319/) (2024)
- [Learning self-triggered controllers with Gaussian processes](https://mlsanity.com/paper/1909.00178/) (2019)
- [Fast IMU-based Dual Estimation of Human Motion and Kinematic Parameters via Progressive In-Network Computing](https://mlsanity.com/paper/2304.05131/) (2023)

## Abstract (arXiv)

Online learning-based control is a promising approach to control uncertain systems, where unknown components are identified during operation to improve control performance. However, resource-intensive online learning algorithms introduce non-negligible computational delays, especially when executed on systems with limited local computational resources. To mitigate this, an in-network online learning-based control structure is employed by deploying the learning-based controller on a remote computation node and connecting it via a communication channel. In this paper, control performance guarantee is first established by deriving tracking error bound for the in-network control architecture, while accounting for computational delays. The derived tracking error bound allows for diverse communication and computation strategies under a specific condition, including time-/event-triggered mechanisms. Additionally, the trade-off between communication and computation performances is shown for a given desired control performance. Furthermore, to enhance the efficiency in both communication and computation, an efficient control framework with an asynchronous event-triggered mechanism in both control and online learning is devised under the existence of computational delay. The proposed event-triggered strategy is proven to achieve the same control performance as time-triggered scenario while excluding Zeno behavior. Finally, we derive an explicit expression of the proposed event-trigger condition for exponentially stabilizable systems, and demonstrate its effectiveness through simulations.