3 citations · 6 across the 14 of their papers we have counts for
6 papers · 1 filter
Learning-based Parameterized Barrier Function for Safety-Critical Control of Unknown Systems
Sihua Zhang, Di-Hua Zhai, Xiaobing Dai +3
With the increasing complexity of real-world systems and varying environmental uncertainties, it is difficult to build an accurate dynamic model, which poses challenges especially…
Kernel-based Learning for Safe Control of Discrete-Time Unknown Systems under Incomplete Observations
Zewen Yang, Xiaobing Dai, Weijie Yang +3
Safe control for dynamical systems is critical, yet the presence of unknown dynamics poses significant challenges. In this paper, we present a learning-based control approach for t…
Learning-based Prescribed-Time Safety for Control of Unknown Systems with Control Barrier Functions
Tzu-Yuan Huang, Sihua Zhang, Xiaobing Dai +4
In many control system applications, state constraint satisfaction needs to be guaranteed within a prescribed time. While this issue has been partially addressed for systems with k…
Decentralized Event-Triggered Online Learning for Safe Consensus of Multi-Agent Systems with Gaussian Process Regression
Xiaobing Dai, Zewen Yang, Mengtian Xu +3
Consensus control in multi-agent systems has received significant attention and practical implementation across various domains. However, managing consensus control under unknown d…
Learning-based Control for PMSM Using Distributed Gaussian Processes with Optimal Aggregation Strategy
Zhenxiao Yin, Xiaobing Dai, Zewen Yang +3
The growing demand for accurate control in varying and unknown environments has sparked a corresponding increase in the requirements for power supply components, including permanen…
Semi-automated Thermal Envelope Model Setup for Adaptive Model Predictive Control with Event-triggered System Identification
Lu Wan, Xiaobing Dai, Torsten Welfonder +2
To reach carbon neutrality in the middle of this century, smart controls for building energy systems are urgently required. Model predictive control (MPC) demonstrates great potent…