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Bo Pang 0002
Person information
- affiliation (PhD 2021): New York University, Tandon School of Engineering, Department of Electrical and Computer Engineering, Control and Networks Lab, Brooklyn, NY, USA
- affiliation (2014 - 2017): Shanghai Jiao Tong University, Department of Automation, Key Laboratory of System Control and Information Processing, Shanghai, China
Other persons with the same name
- Bo Pang — disambiguation page
- Bo Pang 0001 — Google, Mountain View, CA, USA (and 2 more)
- Bo Pang 0003
— Shanghai Jiao Tong University, Department of Computer Science, Machine Vision and Intelligence Group, Shanghai, China
- Bo Pang 0004 — Salesforce Research, USA (and 1 more)
- Bo Pang 0005 — National University of Defense Technology, State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, Changsha, China
- Bo Pang 0006 — Chinese Academy of Sciences, Institute of Semiconductors, State Key Laboratory on Integrated Optoelectronics, Beijing, China
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2020 – today
- 2025
- [j11]Leilei Cui, Bo Pang, Miroslav Krstic, Zhong-Ping Jiang:
Learning-based adaptive optimal control of linear time-delay systems: A value iteration approach. Autom. 171: 111944 (2025) - 2024
- [j10]Leilei Cui
Learning-Based Adaptive Optimal Control of Linear Time-Delay Systems: A Policy Iteration Approach. IEEE Trans. Autom. Control. 69(1): 629-636 (2024) - 2023
- [j9]Bo Pang
Reinforcement Learning for Adaptive Optimal Stationary Control of Linear Stochastic Systems. IEEE Trans. Autom. Control. 68(4): 2383-2390 (2023) - [j8]Tong Liu
A Unified Framework for Data-Driven Optimal Control of Connected Vehicles in Mixed Traffic. IEEE Trans. Intell. Veh. 8(8): 4131-4145 (2023) - [c7]Leilei Cui, Bo Pang, Zhong-Ping Jiang:
Learning Adaptive Optimal Controllers for Linear Time-Delay Systems *. ACC 2023: 4575-4580 - [c6]Leilei Cui, Bo Pang, Zhong-Ping Jiang:
Reinforcement-Learning-Based Risk-Sensitive Optimal Feedback Mechanisms of Biological Motor Control. CDC 2023: 7944-7949 - 2022
- [j7]Bo Pang
Human motor learning is robust to control-dependent noise. Biol. Cybern. 116(3): 307-325 (2022) - [j6]Bo Pang
Robust Policy Iteration for Continuous-Time Linear Quadratic Regulation. IEEE Trans. Autom. Control. 67(1): 504-511 (2022) - [i5]Leilei Cui, Bo Pang, Zhong-Ping Jiang:
Learning-Based Adaptive Optimal Control of Linear Time-Delay Systems: A Policy Iteration Approach. CoRR abs/2210.00204 (2022) - 2021
- [j5]Bo Pang
Adaptive Optimal Control of Linear Periodic Systems: An Off-Policy Value Iteration Approach. IEEE Trans. Autom. Control. 66(2): 888-894 (2021) - [c5]Bo Pang, Zhong-Ping Jiang:
Robust Reinforcement Learning: A Case Study in Linear Quadratic Regulation. AAAI 2021: 9303-9311 - [c4]Tong Liu, Leilei Cui, Bo Pang, Zhong-Ping Jiang:
Data-Driven Adaptive Optimal Control of Mixed-Traffic Connected Vehicles in a Ring Road. CDC 2021: 77-82 - [i4]Bo Pang, Zhong-Ping Jiang:
Reinforcement Learning for Adaptive Optimal Stationary Control of Linear Stochastic Systems. CoRR abs/2107.07788 (2021) - 2020
- [j4]Bo Pang
Reinforcement learning for adaptive optimal control of continuous-time linear periodic systems. Autom. 118: 109035 (2020) - [c3]Bo Pang, Zhong-Ping Jiang:
A Data-driven Approach for Constrained Infinite-Horizon Linear Quadratic Regulation. CDC 2020: 6010-6015 - [i3]Bo Pang
Robust Policy Iteration for Continuous-time Linear Quadratic Regulation. CoRR abs/2005.09528 (2020) - [i2]Bo Pang
Robust Reinforcement Learning: A Case Study in Linear Quadratic Regulation. CoRR abs/2008.11592 (2020)
2010 – 2019
- 2019
- [c2]Bo Pang
Reinforcement Learning for Adaptive Periodic Linear Quadratic Control. CDC 2019: 3322-3327 - [i1]Bo Pang, Zhong-Ping Jiang:
Adaptive Optimal Control of Linear Periodic Systems: An Off-Policy Value Iteration Approach. CoRR abs/1901.08650 (2019) - 2018
- [j3]Yin Cheng, Yuexin Huang, Bo Pang
ThermalNet: A deep reinforcement learning-based combustion optimization system for coal-fired boiler. Eng. Appl. Artif. Intell. 74: 303-311 (2018) - [c1]Bo Pang
Data-driven Finite-horizon Optimal Control for Linear Time-varying Discrete-time Systems. CDC 2018: 861-866 - 2017
- [j2]Bo Pang
LMI Relaxations for Quadratic Stabilization of Guaranteed Cost Control of T-S Fuzzy Systems. Int. J. Fuzzy Syst. 19(5): 1392-1405 (2017) - 2016
- [j1]Bo Pang
Exponentially stable guaranteed cost control for continuous and discrete-time Takagi-Sugeno fuzzy systems. Neurocomputing 205: 210-221 (2016)
Coauthor Index
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