THE DISCRIMINATION OF BARBELL WEIGHT FOR WEIGHTLIFTERS

Ten college weightlifters were recruited in this study. The standard barbell weight (Ws) of each participant was set at 80% of personal best snatch record. The test barbell weights that include Ws, Ws+-1kg, Ws+-2kg, and Ws+-5kg were given randomly, then each lifter was asked to identify the difference between the test weight and standard weight. The discrimination was over 86% when the test weight was Ws+-5kg. For the test weight equal to the standard weight, the discrimination was significantly less than that of other test weights (p less than 01). Based on the results, the weightlifter seems to have good discrimination in the barbell mass at the difference of 5 kg. It seems that they could not be aware of the slight difference (ex: less than 2kg) of barbell mass by 80% of their best snatch record

Haptic identification by ELM-controlled uncertain manipulator

This paper presents an extreme learning machine (ELM) based control scheme for uncertain robot manipulators to perform haptic identification. ELM is used to compensate for the unknown nonlinearity in the manipulator dynamics. The ELM enhanced controller ensures that the closed-loop controlled manipulator follows a specified reference model, in which the reference point as well as the feedforward force is adjusted after each trial for haptic identification of geometry and stiffness of an unknown object. A neural learning law is designed to ensure finite-time convergence of the neural weight learning, such that exact matching with the reference model can be achieved after the initial iteration. The usefulness of the proposed method is tested and demonstrated by extensive simulation studies. Index Terms—Extreme learning machine; haptic identification; adaptive control; robot manipulator

Teleoperation control based on combination of wave variable and neural networks

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 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