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LU SHI

About me

My main focus is the theoretical analysis and implementation of the Koopman Operator in robotics.

I am a 5th-year Ph.D. Candidate in the Department of Electrical and Computer Engineering at the University of California, Riverside (UCR). I work in the ARCS lab with Prof. Karydis Konstantinos. Before joining UCR, I completed my bachelor’s degree in Automation and Control at Xi’an Jiaotong University, China.

Please find me at lshi024@ucr.edu or on LinkedIn for the most recent CV.

Publications

  • Refereed Journal Publications
    L. Shi and K. Karydis, L. Shi* and K. Karydis, ACD-EDMD: Analytical Construction for Dictionaries of Lifting Functions in Koopman Operator-based Nonlinear Robotic Systems. IEEE Robotics and Automation Letters. (RAL+ICRA). 2022
  • Peer-reviewed Conference Proceedings
    L. Shi, C. Mucchiani, and K. Karydis ”Online Modeling and Control of Soft Multi-fingered Grippers via Koopman Operator Theory.” In IEEE International Conference on Automation Science and Engineering (CASE), 2022.
    L. Shi and K. Karydis, Enhancement for Robustness of Koopman Operator-based Data-driven Mobile Robotic Systems. In IEEE Int. Conf. on Robotics and Automation (ICRA, impact factor = 5.6), 2021.
    L. Shi, H. Teng, X. Kan, and K. Karydis, A Data-driven Hierarchical Control Structure for Systems with Uncertainty. In IEEE Conf. on Control Technology and Applications (CCTA, impact factor = 3.46), 2020, pp. 57-63.

Projects

  • Design of the Lifting Functions in Koopman Operator Estimation for Nonlinear Robotic Systems:
    We propose a general and analytical methodology to formalize the construction of lifting functions based on system characteristic properties of a robot and evaluate with a range of diverse nonlinear robotic systems (a wheeled mobile robot, a two-revolute-joint robotic arm, and a soft robotic leg).
  • Analysis on robustness to noise of model extraction and prediction using Koopman Operator:
    We build the theoretical contribution on model extraction and states prediction that proposes a way to quantify the prediction error because of noisy measurements when using Koopman operator to estimate the system. Then a non-holonomic wheeled robot, ROSbot, is simulated in Gazebo to illustrated the performance of the algorithm.
  • Design of an hierarchical control structure with Koopman operator theory to improve performance of robot under uncertainty: The structure is evaluated and justified in the Crazyflie micro-aerial robots that wrapped around the Geometric controller and PID controller.
  • Data-driven Distributed Formation Control for Multiple Quadrotor under Ground Effect:
    We investigate how to use DNN and RL to learn the effect caused by uncertainties as well as interactions and further utilize it in traditional consensus controllers design to achieve formation control goal. Then we test in Matlab with data from Crazyflie robot and compare the networks with different structure as well as input features.