Search Results for author: Qingchen Liu

Found 8 papers, 0 papers with code

Distributed Coverage Control of Constrained Constant-Speed Unicycle Multi-Agent Systems

no code implementations12 Apr 2023 Qingchen Liu, Zengjie Zhang, Nhan Khanh Le, Jiahu Qin, Fangzhou Liu, Sandra Hirche

This paper proposes a novel distributed coverage controller for a multi-agent system with constant-speed unicycle robots (CSUR).

Average Communication Rate for Event-Triggered Stochastic Control Systems

no code implementations13 Jan 2023 Zengjie Zhang, Qingchen Liu, Mohammad H. Mamduhi, Sandra Hirche

Quantifying the average communication rate (ACR) of a networked event-triggered stochastic control system (NET-SCS) with deterministic thresholds is challenging due to the non-stationary nature of the system's stochastic processes.

FedXGBoost: Privacy-Preserving XGBoost for Federated Learning

no code implementations20 Jun 2021 Nhan Khanh Le, Yang Liu, Quang Minh Nguyen, Qingchen Liu, Fangzhou Liu, Quanwei Cai, Sandra Hirche

Federated learning is the distributed machine learning framework that enables collaborative training across multiple parties while ensuring data privacy.

Federated Learning Privacy Preserving

Model-Free Incremental Adaptive Dynamic Programming Based Approximate Robust Optimal Regulation

no code implementations4 May 2021 Cong Li, Yongchao Wang, Fangzhou Liu, Qingchen Liu, Martin Buss

This paper presents a new formulation for model-free robust optimal regulation of continuous-time nonlinear systems.

Value of information in networked control systems subject to delay

no code implementations7 Apr 2021 Siyi Wang, Qingchen Liu, Precious Ugo Abara, John S. Baras, Sandra Hirche

In this paper, we study the trade-off between the transmission cost and the control performance of the multi-loop networked control system subject to network-induced delay.

Scheduling

Distributed Learning Consensus Control for Unknown Nonlinear Multi-Agent Systems based on Gaussian Processes

no code implementations29 Mar 2021 Zewen Yang, Stefan Sosnowski, Qingchen Liu, Junjie Jiao, Armin Lederer, Sandra Hirche

In this paper, a distributed learning leader-follower consensus protocol based on Gaussian process regression for a class of nonlinear multi-agent systems with unknown dynamics is designed.

Gaussian Processes regression

Off-Policy Risk-Sensitive Reinforcement Learning Based Constrained Robust Optimal Control

no code implementations10 Jun 2020 Cong Li, Qingchen Liu, Zhehua Zhou, Martin Buss, Fangzhou Liu

By introducing pseudo controls and risk-sensitive input and state penalty terms, the constrained robust stabilization problem of the original system is converted into an equivalent optimal control problem of an auxiliary system.

reinforcement-learning Reinforcement Learning (RL)

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