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Improvement of the Control Performance of Pneumatic Artificial Muscle Manipulators Using an Intelligent Switching Control Method  

Ahn, Kyoung-Kwan (School of Mechanical and Automotive Engineering, University of Ulsan)
Thanh, TU Diep Cong (School of Mechanical and Automotive Engineering, University of Ulsan)
Publication Information
Journal of Mechanical Science and Technology / v.18, no.8, 2004 , pp. 1388-1400 More about this Journal
Abstract
Problems with the control, oscillatory motion and compliance of pneumatic systems have prevented their widespread use in advanced robotics. However, their compactness, power/weight ratio, ease of maintenance and inherent safety are factors that could be potentially exploited in sophisticated dexterous manipulator designs. These advantages have led to the development of novel actuators such as the McKibben Muscle, Rubber Actuator and Pneumatic Artificial Muscle Manipulators. However, some limitations still exist, such as a deterioration of the performance of transient response due to the changes in the external inertia load in the pneumatic artificial muscle manipulator. To overcome this problem, a switching algorithm of the control parameter using a learning vector quantization neural network (LVQNN) is newly proposed. This estimates the external inertia load of the pneumatic artificial muscle manipulator. The effectiveness of the proposed control algorithm is demonstrated through experiments with different external inertia loads.
Keywords
Pneumatic Artificial Muscle; Neural Network; Switching Control; Intelligent Control;
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Times Cited By KSCI : 2  (Citation Analysis)
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