57 citations · 238 across the 15 of their papers we have counts for
8 papers · 1 filter
Data-driven Self-triggered Control via Trajectory Prediction
Wenjie Liu, Jian Sun, Gang Wang +2
Self-triggered control, a well-documented technique for reducing the communication overhead while ensuring desired system performance, is gaining increasing popularity. However, ex…
Data-Driven Resilient Predictive Control under Denial-of-Service
Wenjie Liu, Jian Sun, Gang Wang +2
The study of resilient control of linear time-invariant (LTI) systems against denial-of-service (DoS) attacks is gaining popularity in emerging cyber-physical applications. In prev…
Data-driven Control of Dynamic Event-triggered Systems with Delays
Xin Wang, Jian Sun, Julian Berberich +3
This paper studies data-driven control of unknown sampled-data systems with communication delays under an event-triggering transmission mechanism. Data-based representations for ti…
Resilient Control under Quantization and Denial-of-Service: Co-designing a Deadbeat Controller and Transmission Protocol
Wenjie Liu, Jian Sun, Gang Wang +2
This paper is concerned with the problem of stabilizing continuous-time linear time-invariant systems subject to quantization and Denial-of-Service (DoS) attacks. In this context,…
Resonant Beam Communications: Principles and Designs
Mingliang Xiong, Qingwen Liu, Gang Wang +2
Wireless optical communications (WOC) has carriers up to several hundred terahertz, which offers several advantages, such as ultrawide bandwidth and no electromagnetic interference…
Gauss-Newton Unrolled Neural Networks and Data-driven Priors for Regularized PSSE with Robustness
Qiuling Yang, Alireza Sadeghi, Gang Wang +2
Distributed renewable generation, elastic loads, and purposeful manipulation of meter readings challenge the monitoring and control of today's power systems (PS). In this context,…
A Statistical Learning Approach to Reactive Power Control in Distribution Systems
Qiuling Yang, Alireza Sadeghi, Gang Wang +2
Pronounced variability due to the growth of renewable energy sources, flexible loads, and distributed generation is challenging residential distribution systems. This context, moti…
Two-Timescale Voltage Control in Distribution Grids Using Deep Reinforcement Learning
Qiuling Yang, Gang Wang, Alireza Sadeghi +2
Modern distribution grids are currently being challenged by frequent and sizable voltage fluctuations, due mainly to the increasing deployment of electric vehicles and renewable ge…