Event-Triggered Control

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Event-triggered control studies how to choose when a controller updates or sends information, instead of acting at fixed time intervals, to save communication, computation, and energy. Common approaches use state- or error-dependent thresholds, asynchronous networked updates, and safety tools such as control barrier functions; newer methods use reinforcement learning or neural networks to adapt triggering decisions. Applications include networked systems, multi-agent coordination, and robotic manipulation, with evaluations ranging from stability and tracking guarantees to simulations and hardware experiments. A central challenge is reducing updates without losing performance or safety under uncertainty and delays, while proving stability and avoiding infinitely frequent updates.

Latest papers

12 papers0 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Event-triggered Control and Online Learning for Networked Systems under Computational Delays
Aug 30, 2026Xiaobing Dai, Armin Lederer, Zewen Yang +4Event-Triggered Control Feedback Control
Chair of Information-oriented Control, School of Computation, Information and Technology, Technical University of Munich, Germany · Learning and Adaptive Systems Group, Department of Computer Science, ETH Zurich, Switzerland · Chair of Robotics and Systems Intelligence, Munich Institute of Robotics and Machine Intelligence, Technical University of Munich +2
VLA-Corrector: Lightweight Detect-and-Correct Inference for Adaptive Action Horizon
Jul 2, 2026Yi Pan, Miao Pan, Qi Lu +8Long-Horizon Robotic Manipulation Feedback Control
Zhejiang University · Alibaba DAMO Academy
Deep Reinforcement Learning-Enhanced Event-Triggered Data-Driven Predictive Control for a 3D Cable-Driven Soft Robotic Arm
Jun 24, 2026Cheng Ouyang, Moeen Ul Islam, Kaixiang Zhang +3RL Control Robot Trajectory Tracking
Department of Agricultural & Biological Engineering, Mississippi State University, MS, USA · Department of Mechanical Engineering, Michigan State University, MI, USA · Department of Electrical and Computer Engineering, Michigan State University, MI, USA
SPIKE: An Adaptive Dual Controller Framework for Cost-Efficient Long-Horizon Game Agents
May 18, 2026Wencan Jiang, Jiangning Zhang, Jianbiao Mei +6Game-Playing Agents Feedback Control
1Zhejiang University · 2National University of Singapore · 3Nanyang Technological University
Priority-Driven Control and Communication in Decentralized Multi-Agent Systems via Reinforcement Learning
May 11, 2026Qingyun Guo, Junyi Shi, Tomasz Piotr Kucner +1Agent Communication Protocols Feedback Control
Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland · Center for Artificial Intelligence, Helsinki, Finland
When Descent Is Too Stable: Event-Triggered Hamiltonian Learning to Optimize
May 7, 2026Yi Wang, Chandrajit BajajDynamical Systems Learning to Optimize
1Oden Institute · Department of Computer Science
Bio-plausible Neuromorphic Disturbance Observer Based on Emulation Theory: Extended Version
May 5, 2026Hongfu Xu, Xiaoyu Guo, Shengbo Wang +1Feedback Control Adaptive Control
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China · Department of Mechanical Engineering, City University of Hong Kong, Hong Kong · Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong +1
Neural Network-Based Adaptive Event-Triggered Control for Dual-Arm Unmanned Aerial Manipulator Systems
Apr 18, 2026Yang Wang, Hai Yu, Wei He +3Robot Trajectory Tracking Aerial Robotics
Institute of Robotics and Automatic Information System, College of Artificial Intelligence, and Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, China. · Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen 518083, China.
Periodic Event-Triggered Explicit Reference Governor for Constrained Attitude Control on SO(3)
Apr 5, 2026Satoshi Nakano, Masahiro Suzuki, Misa Ohashi +2Feedback Control Event-Triggered Control
The authors are with the Department of Engineering, Nagoya Institute of Technology, Nagoya, 4668555 Aichi, Japan
Safe Consensus of Cooperative Manipulation with Hierarchical Event-Triggered Control Barrier Functions
Mar 6, 2026Simiao Zhuang, Bingkun Huang, Zewen YangControl Barrier Functions Multi-Agent Control
Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich (TUM), 80992 Munich, Germany
Dual-Agent Multiple-Model Reinforcement Learning for Event-Triggered Human-Robot Co-Adaptation in Decoupled Task Spaces
Mar 6, 2026Yaqi Li, Zhengqi Han, Huifang Liu +1Human-Robot Interaction RL for Robotics
Faculty of Engineering and IT, University of Technology Sydney, Australia · College of Medical information and Artificial intelligence, Shandong First Medical University (Shandong Academy of Medical Science) 6699 Qingdao Road, Jinan, Shandong, China
Distributed Event-Triggered Distance-Based Formation Control for Multi-Agent Systems
Sep 15, 2025Evangelos Psomiadis, Panagiotis TsiotrasMulti-Robot Formation Control Multi-Agent Control
D. Guggenheim School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, 30332-0150, USA

Aug 30, 2026J/K move · Enter open · S save

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

Xiaobing Dai, Armin Lederer, Zewen Yang, Sihua Zhang+3

Chair of Information-oriented Control, School of Computation, Information and Technology, Technical University of Munich, Germany · Learning and Adaptive Systems Group, Department of Computer Science, ETH Zurich, Switzerland · Chair of Robotics and Systems Intelligence, Munich Institute of Robotics and Machine Intelligence, Technical University of Munich+2

Event-Triggered Control Feedback Control

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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.