Improvement of control performance of pneumatic artificial muscle manipulator using intelligent switching control

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 potentially be 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 deterioration of the performance of transient response due to the change the external inertia load in the pneumatic artificial muscle manipulator. To overcome this problem, switching algorithm of control parameter using learning vector quantization neural network (LVQNN) is newly proposed, which estimates the external inertia load of the pneumatic artificial muscle manipulator. The effectiveness of the proposed control algorithms is demonstrated through experiments with different external inertia loads.