Neural Control of Bimanual Robots With Guaranteed Global Stability and Motion Precision

By Chenguang Yang, Yiming Jiang, Zhijun Li, Wei He, Chun-Yi Su. IEEE Transactions on Industrial Informatics, 2017

365 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/53348e15689977e33d0138bfa90ba1d255770634

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Wei He
2. Yiting Dong
3. Zhijun Li
4. Chenguang Yang
5. Changyin Sun
6. Min Wang
7. Shi-lu Dai
8. Shude He

In Mechanical Engineering

1. Wei He
2. Shi-lu Dai
3. Shude He

Citations in the corpus

• Leader–Follower Formation Control of USVs With Prescribed Performance and Collision Avoidance (2019, background): “, robot manipulators [23]–[25] and marine vehicles [26], where the prescribed performance controllers were designed in a centralized manner.”
• Robot Learning System Based on Adaptive Neural Control and Dynamic Movement Primitives (2019, methodology): “NNs have been widely used in controller design because of their approximation ability [20]–[22].”
• Platoon Formation Control With Prescribed Performance Guarantees for USVs (2018, background): “, robot manipulators [17]–[19], marine surface vehicles [20], [21], underwater vehicles [22], etc.”
• Adaptive Neural Network Control of a Flapping Wing Micro Aerial Vehicle With Disturbance Observer (2017, methodology): “In 2016, Yang et al. [38], [39] used NN approximation techniques to compensate the unknown dynamics of both the robot arms and manipulated objects by the Baxter robot.”
• Prescribed Performance Control of Uncertain Euler–Lagrange Systems Subject to Full-State Constraints (2018, neutral): “[34], [35] have also contributed to the prescribed tracking performance for robotic manipulator, resulting in the tracking error converges to a prescribed compact set around zero as t → ∞ with an exponential rate.”
• Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning (2018, background): “However, while the robotic end-effector comes in contact with the environment, it is inevitable that an interaction force will develop between the robot and its environment [40]–[42].”