Adaptive Impedance Control for an Upper Limb Robotic Exoskeleton Using Biological Signals

By Zhijun Li, Zhicong Huang, Wei He, Chun-Yi Su. IEEE transactions on industrial electronics (1982. Print), 2017

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Adaptive Neural Network Control of a Robotic Manipulator With Time-Varying Output Constraints (2017, background): “Universally, constraints exist both in the input and the output of the robotic system, such as saturation, dead-zone, and safety specifications [4]–[6].”
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].”
Manipulability Optimization of Redundant Manipulators Using Dynamic Neural Networks (2017, methodology): “Neural networks, which feature capabilities of high-speed parallel distributed processing, and can be readily implemented by hardware, have been recognized as a powerful tool for realtime processing and successfully applied widely in various control systems [3], [5], [8], [12], [19]–[26].”
Adaptive Neural Network Control of a Flapping Wing Micro Aerial Vehicle With Disturbance Observer (2017, methodology): “…we obtain Because of the property 1, we have 2 ) e Tr 2 e r 2 and we can obtain According to the description of the basis functions of Gaussian radial basis function neural network in [53], we can get the equation where (cid:12) is a positive constant, and S t is the bounded vector function.”