Shu Li
Orcid: 0000-0002-5828-3010Affiliations:
- Harbin Institute of Technology, State Key Laboratory of Robotics and System, China
- Liaoning University of Technology, College of Science, Jinzhou, China (former)
According to our database1,
Shu Li
authored at least 18 papers
between 2015 and 2022.
Collaborative distances:
Collaborative distances:
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Bibliography
2022
Adaptive Fuzzy Finite-Time Tracking Control for Nonstrict Full States Constrained Nonlinear System With Coupled Dead-Zone Input.
IEEE Trans. Cybern., 2022
A 2-year locomotive exploration and scientific investigation of the lunar farside by the Yutu-2 rover.
Sci. Robotics, 2022
Velocity Following Control of a Pseudo-Driven Wheel for Reducing Internal Forces Between Wheels.
IEEE Robotics Autom. Lett., 2022
2021
Adaptive Neural Network-Based Finite-Time Tracking Control for Nonstrict Nonaffined MIMO Nonlinear Systems.
IEEE Trans. Syst. Man Cybern. Syst., 2021
Adaptive Neural Network-Based Finite-Time Online Optimal Tracking Control of the Nonlinear System With Dead Zone.
IEEE Trans. Cybern., 2021
2020
Reinforcement Learning Neural Network-Based Adaptive Control for State and Input Time-Delayed Wheeled Mobile Robots.
IEEE Trans. Syst. Man Cybern. Syst., 2020
Adaptive Partial Reinforcement Learning Neural Network-Based Tracking Control for Wheeled Mobile Robotic Systems.
IEEE Trans. Syst. Man Cybern. Syst., 2020
Definition and Application of Variable Resistance Coefficient for Wheeled Mobile Robots on Deformable Terrain.
IEEE Trans. Robotics, 2020
ADP-Based Online Tracking Control of Partially Uncertain Time-Delayed Nonlinear System and Application to Wheeled Mobile Robots.
IEEE Trans. Cybern., 2020
Adaptive NN-based finite-time tracking control for wheeled mobile robots with time-varying full state constraints.
Neurocomputing, 2020
2019
Adaptive Reinforcement Learning Control Based on Neural Approximation for Nonlinear Discrete-Time Systems With Unknown Nonaffine Dead-Zone Input.
IEEE Trans. Neural Networks Learn. Syst., 2019
2018
Adaptive neural network tracking control-based reinforcement learning for wheeled mobile robots with skidding and slipping.
Neurocomputing, 2018
Proceedings of the IEEE International Conference on Real-time Computing and Robotics, 2018
2017
Adaptive Neural Network-Based Tracking Control for Full-State Constrained Wheeled Mobile Robotic System.
IEEE Trans. Syst. Man Cybern. Syst., 2017
Neural Approximation-Based Adaptive Control for a Class of Nonlinear Nonstrict Feedback Discrete-Time Systems.
IEEE Trans. Neural Networks Learn. Syst., 2017
2016
Adaptive control of nonlinear systems with full state constraints using Integral Barrier Lyapunov Functionals.
Neurocomputing, 2016
Neural network-based adaptive control for a class of chemical reactor systems with non-symmetric dead-zone.
Neurocomputing, 2016
2015
Adaptive neural network tracking design for a class of uncertain nonlinear discrete-time systems with unknown time-delay.
Neurocomputing, 2015