Browsing by Author "Liu, Hsiu-Jen"

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An Adaptive Upper-Arm EMG-Based Robot Control System
(2010-09-01)
Liu, Hsiu-Jen
;
Young, Kuu-Young
;
電機工程學系
;
Department of Electrical and Computer Engineering
The human-assisting robot can be helpful for improving the life quality of the disabled and elderly. As Electromyography (EMG) is a physiological signal generated during muscle contraction, it implicates, to certain extent, the human intention for movement, and is thus very suitable to serve as the control signal for the assisting robot. In this paper, we develop an upper-arm EMG-based robot control system, which provides a natural and intuitive manipulation. To satisfy the demand of real-time control, we propose a simple and effective method for the mapping between the upper-arm EMG signal and corresponding movement. However, due to the fuzziness inherent in the EMG signals, which are time-varying and highly nonlinear, the tuning for system parameters is not that straightforward. We therefore employ the concept of fuzzy system to find proper parameters that better suit for the individual users. To provide better adaptive capability, we propose using the adaptive neuro-fuzzy inference system (ANFIS) to realize the fuzzy system. We perform a series of experiments to demonstrate the effectiveness of the proposed adaptive upper-arm EMG-based robot control system.
APPLYING WAVE-VARIABLE-BASED SLIDING MODE IMPEDANCE CONTROL FOR ROBOT TELEOPERATION
(2011)
Liu, Hsiu-Jen
;
Young, Kuu-Young
;
電機工程學系
;
Department of Electrical and Computer Engineering
The wave variable approach has the appealing features of not requiring a priori knowledge on both time delay and master/slave plants and simplicity in parameter adjustment, when applied for robot teleoperation. However, its current applications are mainly related to tasks involving position tracking. In this paper, we propose integrating the wave variable approach with the sliding mode impedance controller, so that it can be further applied for robot compliance tasks, in which simultaneous control of both position and force are commanded and contact with the environment is inevitable. We start with the validation of the stability of the proposed wave-variable-based sliding mode impedance control scheme. For demonstration, we apply the proposed system to perform the command-following compliance task in both simulation and experiment.
Robot Motion Governing Using Upper Limb EMG Signal Based on Empirical Mode Decomposition
(2010)
Liu, Hsiu-Jen
;
Young, Kuu-young
;
電機工程學系
;
Department of Electrical and Computer Engineering
This paper presents a simple and effective approach to govern robot arm motion in real time using upper limb EMG signals. Considering the non-stationary and nonlinear characteristics of the EMG signals, in the design for feature extraction, we introduce the empirical mode decomposition (EMD) to decompose the EMG signals into intrinsic mode functions (IMFs). Each IMF represents different physical characteristic, so that the muscular movement can be recognized. We then integrate it with a so-called initial point detection method previously proposed to establish the mapping between the upper limb EMG signals and corresponding robot arm movements in real time. In addition, for each individual user, we adopt a fuzzy approach to select proper system parameters for motion classification. The experimental results show the feasibility of the proposed approach with accurate motion recognition.
Upper-Limb EMG-Based Robot Motion Governing Using Empirical Mode Decomposition and Adaptive Neural Fuzzy Inference System
(2012-12-01)
Liu, Hsiu-Jen
;
Young, Kuu-Young
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
To improve the quality of life for the disabled and elderly, this paper develops an upper-limb, EMG-based robot control system to provide natural, intuitive manipulation for robot arm motions. Considering the non-stationary and nonlinear characteristics of the Electromyography (EMG) signals, especially when multi-DOF movements are involved, an empirical mode decomposition method is introduced to break down the EMG signals into a set of intrinsic mode functions, each of which represents different physical characteristics of muscular movement. We then integrate this new system with an initial point detection method previously proposed to establish the mapping between the EMG signals and corresponding robot arm movements in real-time. Meanwhile, as the selection of critical values in the initial point detection method is user-dependent, we employ the adaptive neuro-fuzzy inference system to find proper parameters that are better suited for individual users. Experiments are performed to demonstrate the effectiveness of the proposed upper-limb EMG-based robot control system.
Upper-Limb EMG-Based Robot Motion Governing Using Empirical Mode Decomposition and Adaptive Neural Fuzzy Inference System
(2012-12-01)
Liu, Hsiu-Jen
;
Young, Kuu-Young
;
電子工程學系及電子研究所
;
電機工程學系
;
Department of Electronics Engineering and Institute of Electronics
To improve the quality of life for the disabled and elderly, this paper develops an upper-limb, EMG-based robot control system to provide natural, intuitive manipulation for robot arm motions. Considering the non-stationary and nonlinear characteristics of the Electromyography (EMG) signals, especially when multi-DOF movements are involved, an empirical mode decomposition method is introduced to break down the EMG signals into a set of intrinsic mode functions, each of which represents different physical characteristics of muscular movement. We then integrate this new system with an initial point detection method previously proposed to establish the mapping between the EMG signals and corresponding robot arm movements in real-time. Meanwhile, as the selection of critical values in the initial point detection method is user-dependent, we employ the adaptive neuro-fuzzy inference system to find proper parameters that are better suited for individual users. Experiments are performed to demonstrate the effectiveness of the proposed upper-limb EMG-based robot control system.