Cong Li

Orcid: 0000-0002-1103-4818

Affiliations:
  • Technical University of Munich, Munich, Germany


According to our database1, Cong Li authored at least 13 papers between 2020 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
Adaptive Deep Koopman Operators for Soft Robot Control: Application to Luban Lock Disassembly.
IEEE Trans. Ind. Electron., May, 2026

Multirate Distributed Receding Horizon Reinforcement Learning for Optimal UAV-UGV Formation Control.
IEEE Trans. Artif. Intell., March, 2026

2025
Modeling propagation competition between hostile influential groups using opinion dynamics.
Autom., January, 2025

Toward Scalable Multirobot Control: Fast Policy Learning in Distributed MPC.
IEEE Trans. Robotics, 2025

2024
Safe Planning and Control Under Uncertainty: A Model-Free Design With One-Step Backward Data.
IEEE Trans. Ind. Electron., 2024

2023
Safe Feedback Motion Planning in Unknown Environments: An Instantaneous Local Control Barrier Function Approach.
J. Intell. Robotic Syst., October, 2023

Off-Policy Risk-Sensitive Reinforcement Learning-Based Constrained Robust Optimal Control.
IEEE Trans. Syst. Man Cybern. Syst., April, 2023

2022
Concurrent Learning-Based Adaptive Control of an Uncertain Robot Manipulator With Guaranteed Safety and Performance.
IEEE Trans. Syst. Man Cybern. Syst., 2022

2021
Incremental Adaptive Dynamic Programming for Approximate Optimal Tracking Control: a Decoupled and Model-Free Approach.
CoRR, 2021

Instantaneous Local Control Barrier Function: An Online Learning Approach for Collision Avoidance.
CoRR, 2021

Model-Free Incremental Adaptive Dynamic Programming Based Approximate Robust Optimal Regulation.
CoRR, 2021

2020
Off Policy Risk Sensitive Reinforcement Learning Based Optimal Tracking Control with Prescribe Performances.
CoRR, 2020

Online single artificial neural network adaptive critic learning under additive disturbance, state constraints and input saturation.
CoRR, 2020


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