Teleoperation Control Based on Combination of Wave Variable and Neural Networks

Abstract

Abstract-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.

Learning motion primitives from demonstration

“…With the developments of commercial available robotic arms: iARM 25 (produced by Exact Dynamics Ò ) and JACO 26 (produced by Kinova Ò ), a lot of researchers both at home and abroad carried out a series of studies, such as the FRIEND series robots 27 in the University of Bremen, the WMRA-I and II 28 in the University of South Florida, the wheelchair-mounted robotic manipulators (WMRMs) in Purdue University, 29 and the WMRA system ''WIM'' 3,4 in Waseda University. At present, the researches on the WMRA mainly focus on the following categories: (1) develop a variety of human-robot interaction interfaces that can satisfy the motor ability of different users, particularly for the disables; 1,4,[30][31][32] (2) adopt the visual servo technology or learn from demonstration to control the motion of robotic arm, so as to reduce the physical and mental burden of the users; 27,33,34 (3) design a new structure of robotic arm in order to improve the safety of the user, for the users are in the working space of the robotic arm; 35 and (4) develop the integrated WMRA with two or more above characteristics. To develop the WMRA with the characteristics of high safety, high intelligence, and operational ease is the developing trend in the field of assistive robots.…”

Dynamic Learning from Adaptive Neural Control of Uncertain Robots with Guaranteed Full-State Tracking Precision

“…This means ‖ 2 ‖ ≤ * , * is any small value depending on design parameters , , 20 , and 2∞ . Using the bounded property of 2 , we can obtain that…”

“…In order to enhance system robustness on the uncertain parameters in the presence of external disturbances, sliding mode control [8,9] has been proposed to obtain the desired robotic tracking control performance. Owing to the universal approximation property [10][11][12][13][14][15][16][17], a great number of intelligent control schemes, such as adaptive neural/fuzzy control, have been developed for controlling robotic systems with uncertain nonlinearities [18][19][20][21][22].…”

Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope

“…These intelligent control methods improve the performance of gyroscopes, so that the applications of MEMS gyroscopes are expanded. With the vigorous development of nonlinear system control methods [1][2][3][4][5][6][7][8], a variety of gyroscopic modal control methods emerged.…”