冀晓强

研究助理教授

教育背景

博士（美国哥伦比亚大学）

研究领域
智能控制系统，人工智能
学术领域
人工智能与机器人，计算机工程，电子工程，数学与应用数学
电子邮件
jixiaoqiang@cuhk.edu.cn
个人简介

冀晓强教授在美国哥伦比亚大学获得博士学位，现任香港中文大学（深圳）理工学院研究助理教授，兼任深圳市人工智能与机器人研究院高性能智能计算与控制中心负责人，并担任中国仿真学会智能物联专委会委员等。

他的研究主要集中在智能控制系统，主持多项科研及人才项目，至今发表了四十余篇期刊及会议文章。特别是在机器人与非最小相位系统方面，是该领域学习控制方法设计的推动者之一。他担任包括Automatica、IEEE T-ACON、IEEE/ASME T-MECH、IEEE T-ASE在内的多个顶级期刊和会议的审稿人，并于近期获得IEEE CINT优秀论文奖、ISUI最佳论文奖等。

冀教授带领的人工智能控制与决策实验室，是一个学科交叉平台，需要深度融合控制论、人工智能、机器人学、高性能计算、大数据等基础科学。

 

学术著作

(*) denotes the corresponding author:

  1. Xiaoqiang. Ji*, S. Zhu, Y. Xu, and R. Longman. Lifted time stable inversion based feedforward control for linear non-minimum phase systems, Automatica, provisionally accepted, 2024.
  2. Xiaoqiang. Ji, X.Zhang, S.Zhu, F.Deng and B.Zhu. Data-driven adaptive consensus control for heterogeneous nonlinear multi-agent systems using online reinforcement learning, Neurocomputing, Vol. 596, 127818, 2024.
  3. J.Li, C.Zhao, Xiaoqiang. Ji*, M.Li, G.Lu, Y.Xu, and D.Zhang. Multi-view instance attention fusion network for classification, Information Fusion, Vol. 101, 101974, 2024.
  4. K.Xue, Xiaoqiang.Ji*, D.Qu, Y.Peng and H.Qian*. Oboat: An agile omnidirectional robotic platform for unmanned surface vehicle tasks, IEEE/ASME Transactions on Mechatronics, vol.28, no.5, pp.2413-2424, Oct. 2023.
  5. S. Zhu, Y. Wang, B. Zhu, and Xiaoqiang. Ji*. Tracking error boundary of novel stable inversion based feedforward control for a class of non-minimum phase systems, CINT, vol 1714, Springer, 2023.
  6. C. Tan, Xiaoqiang. Ji*, et. al. Multi-Agent path finding algorithm of merged node rule and bounded focal search, CINT, vol 1714, 2023, Springer.
  7. K.Xue, J. Liu, N. Xiao, Xiaoqiang. Ji*, and H. Qian*. A bio-inspired simultaneous surface and underwater risk assessment method based on stereo vision for USVs in nearshore clean waters, IEEE Robotics and Automation Letters (RA-L), 2022.
  8. Xiaoqiang. Ji, and Richard Longman*. Two new stable inverses of discrete time systems, Astrodynamics Specialist Conference, AAS/American Institute of Aeronautics and Astronautics (AIAA), vol. 171(1), 2020, pp. 4137-4143.
  9. Xiaoqiang. Ji, and Richard Longman*. The insensitivity of the iterative learning control inverse problem to initial run when stabilized by a new stable inverse. Modeling, Simulation and Optimization of Complex Process, Bock. et al., Springer, 2(4):257-275, 2020