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Found 6 papers, 1 papers with code
Kernel-based Learning for Safe Control of Discrete-Time Unknown Systems under Incomplete Observations
Safe control for dynamical systems is critical, yet the presence of unknown dynamics poses significant challenges.
Decentralized Event-Triggered Online Learning for Safe Consensus of Multi-Agent Systems with Gaussian Process Regression
Gaussian processes are harnessed to compensate for the unknown components of the multi-agent system.
Cooperative Learning with Gaussian Processes for Euler-Lagrange Systems Tracking Control under Switching Topologies
This work presents an innovative learning-based approach to tackle the tracking control problem of Euler-Lagrange multi-agent systems with partially unknown dynamics operating under switching communication topologies.
Whom to Trust? Elective Learning for Distributed Gaussian Process Regression
This paper introduces an innovative approach to enhance distributed cooperative learning using Gaussian process (GP) regression in multi-agent systems (MASs).
Learning-based Control for PMSM Using Distributed Gaussian Processes with Optimal Aggregation Strategy
The growing demand for accurate control in varying and unknown environments has sparked a corresponding increase in the requirements for power supply components, including permanent magnet synchronous motors (PMSMs).
MOSGA: Modular Open-Source Genome Annotator
The generation of high-quality assemblies, even for large eukaryotic genomes, has become a routine task for many biologists thanks to recent advances in sequencing technologies.
