Teleoperation Control Based on Combination of Wave Variable and Neural Networks

By Chenguang Yang, Xingjian Wang, Zhijun Li, Yanan Li, Chun-Yi Su. IEEE Transactions on Systems, Man & Cybernetics. Systems, 2017

302 citations recorded by the source corpus.

In this paper, a novel control scheme is developed for a teleoperation system, combining the radial basis function (RBF) neural networks (NNs) and wave variable technique to simultaneously compensate for the effects caused by communication delays and dynamics uncertainties. The teleoperation system is set up with a TouchX joystick as the master device and a simulated Baxter robot arm as the slave robot. The haptic feedback is provided to the human operator to sense the interaction force between the slave robot and the environment when manipulating the stylus of the joystick. To utilize the workspace of the telerobot as much as possible, a matching process is carried out between the master and the slave based on their kinematics models. The closed loop inverse kinematics (CLIK) method and RBF NN approximation technique are seamlessly integrated in the control design. To overcome the potential instability problem in the presence of delayed communication channels, wave variables and their corrections are effectively embedded into the control system, and Lyapunov-based analysis is performed to theoretically establish the closed-loop stability. Comparative experiments have been conducted for a trajectory tracking task, under the different conditions of various communication delays. Experimental results show that in terms of tracking performance and force reflection, the proposed control approach shows superior performance over the conventional methods.

Subfields: Biomedical Engineering, Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/a1de196d4457324b5fdaf0e17b771036a6238c61

How researchers see this paper

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

In Biomedical Engineering

1. Wei He
2. Haifeng Huang
3. Zhijun Li
4. Shuzhi Sam Ge

In Control and Systems Engineering

1. Wei He
2. Chenguang Yang
3. Zhijun Li
4. Shuzhi Sam Ge
5. B. Xu
6. Haifeng Huang
7. Yiming Jiang
8. Jing Na

In Mechanical Engineering

1. Wei He
2. Shuzhi Sam Ge

Citations in the corpus

• Robot Learning System Based on Adaptive Neural Control and Dynamic Movement Primitives (2019, unknown): “RBFNN is an effective tool to approximate any continuous function g : Rm → R as follows [32]: g(θ) = W T S(θ) + ε(θ), ∀θ ∈ θ (27)”
• Adaptive Neural Network Control of a Robotic Manipulator With Time-Varying Output Constraints (2017, methodology): “[43], [44] employed RBF NNs to compensate the effect caused by dynamics uncertainties in a teleoperation system.”
• Adaptive Parameter Estimation and Control Design for Robot Manipulators With Finite-Time Convergence (2018, methodology): “A S ONE of the most promising techniques, robotics have been extensively studied by industrial and academic communities in recent years [1]–[5], while automated robots have been widely allocated in the modern manufacturing and factories, e.”