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Found 9 papers, 0 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.
Learning-based Prescribed-Time Safety for Control of Unknown Systems with Control Barrier Functions
In many control system applications, state constraint satisfaction needs to be guaranteed within a prescribed time.
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).
Can Learning Deteriorate Control? Analyzing Computational Delays in Gaussian Process-Based Event-Triggered Online Learning
When the dynamics of systems are unknown, supervised machine learning techniques are commonly employed to infer models from data.
Cooperative Online Learning for Multi-Agent System Control via Gaussian Processes with Event-Triggered Mechanism: Extended Version
In the realm of the cooperative control of multi-agent systems (MASs) with unknown dynamics, Gaussian process (GP) regression is widely used to infer the uncertainties due to its modeling flexibility of nonlinear functions and the existence of a theoretical prediction error bound.
Fast IMU-based Dual Estimation of Human Motion and Kinematic Parameters via Progressive In-Network Computing
We adopt the concept of field Kalman filtering, where the dual estimation problem is decomposed into a fast state estimation process and a computationally expensive parameter estimation process.
