Chao Ning
Orcid: 0000-0002-3064-9551Affiliations:
- Shanghai Jiao Tong University, Department of Automation, MoE Key Laboratory of System Control and Information Processing, China
- Cornell University, School of Chemical and Biomolecular Engineering, Ithaca, NY, USA (PhD 2020)
According to our database1,
Chao Ning
authored at least 25 papers
between 2016 and 2024.
Collaborative distances:
Collaborative distances:
Timeline
Legend:
Book In proceedings Article PhD thesis Dataset OtherLinks
Online presence:
-
on orcid.org
On csauthors.net:
Bibliography
2024
Online Data-Stream-Driven Distributionally Robust Optimal Energy Management for Hydrogen-Based Multimicrogrids.
IEEE Trans. Ind. Informatics, March, 2024
A Risk-Averse Just-In-Time Scheme for Learning-Based Operation of Microgrids with Coupled Electricity-Hydrogen-Ammonia under Uncertainties.
CoRR, 2024
Online-Learning-Based Distributionally Robust Motion Control with Collision Avoidance for Mobile Robots.
Proceedings of the IEEE International Conference on Robotics and Automation, 2024
Proceedings of the Forty-first International Conference on Machine Learning, 2024
2023
IEEE Control. Syst. Lett., 2023
2022
Multistage Scheduling of Regional Power Grids Against Sequential Outage and Power Uncertainties.
IEEE Trans. Smart Grid, 2022
Distributionally Robust Day-ahead Scheduling for Power-traffic Network under a Potential Game Framework.
CoRR, 2022
Streaming-Data-Driven Distributionally Robust Joint Operation of Multi-Microgrids and Off-Site Hydrogen Refueling Stations under Uncertainties.
Proceedings of the IEEE PES Innovative Smart Grid Technologies - Asia, 2022
2021
Distributionally Robust Day-ahead Scheduling for Power-traffic Network Considering Multiple Uncertainties under a Potential Game Framework.
CoRR, 2021
Online learning based risk-averse stochastic MPC of constrained linear uncertain systems.
Autom., 2021
Data-Driven Ambiguous Joint Chance Constrained Economic Dispatch with Correlated Wind Power Uncertainty.
Proceedings of the 2021 American Control Conference, 2021
2020
A transformation-proximal bundle algorithm for multistage adaptive robust optimization and application to constrained robust optimal control.
Autom., 2020
2019
Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming.
Comput. Chem. Eng., 2019
Industrial Steam Systems Optimization under Uncertainty Using Data-Driven Adaptive Robust Optimization.
Proceedings of the 2019 American Control Conference, 2019
Data-Driven Adaptive Robust Optimization Framework for Unit Commitment under Renewable Energy Generation Uncertainty.
Proceedings of the 2019 American Control Conference, 2019
Chemical Process Scheduling under Disjunctive Uncertainty Using Data-Driven Multistage Adaptive Robust Optimization.
Proceedings of the 2019 American Control Conference, 2019
2018
A Transformation-Proximal Bundle Algorithm for Solving Large-Scale Multistage Adaptive Robust Optimization Problems.
CoRR, 2018
Data-driven decision making under uncertainty integrating robust optimization with principal component analysis and kernel smoothing methods.
Comput. Chem. Eng., 2018
Data-Driven Stochastic Robust Optimization: General Computational Framework and Algorithm Leveraging Machine Learning for Optimization under Uncertainty in the Big Data Era.
Comput. Chem. Eng., 2018
Adaptive robust optimization with minimax regret criterion: Multiobjective optimization framework and computational algorithm for planning and scheduling under uncertainty.
Comput. Chem. Eng., 2018
A Transformation-Proximal Bundle Algorithm for Solving Multistage Adaptive Robust Optimization Problems.
Proceedings of the 57th IEEE Conference on Decision and Control, 2018
Data-Driven Adaptive Robust Optimization Framework Based on Principal Component Analysis.
Proceedings of the 2018 Annual American Control Conference, 2018
2017
Data-Driven Nested Stochastic Robust Optimization: A General Computational Framework and Algorithm for Optimization under Uncertainty in the Big Data Era.
CoRR, 2017
Leveraging big data for adaptive robust optimization of scheduling under uncertainty.
Proceedings of the 2017 American Control Conference, 2017
2016
Data-driven robust MILP model for scheduling of multipurpose batch processes under uncertainty.
Proceedings of the 55th IEEE Conference on Decision and Control, 2016