Intelligent switching control of a pneumatic muscle robot arm using learning vector quantization neural network

Pneumatic cylinders are one of the low-cost actuation sources used in industrial and prosthetic application, since they have a high power/weight ratio, high-tension force and long durability. However, problems with the control, oscillatory motion and compliance of pneumatic systems have prevented their widespread use in advanced robotics. To overcome these shortcomings, a number of newer pneumatic actuators have been developed, such as the McKibben Muscle, Rubber Actuator and Pneumatic Artificial Muscle (PAM) Manipulators. In this paper, the solution for position control of a robot arm with slow motion driven by two pneumatic artificial muscles is presented. However, some limitations still exist, such as a deterioration of the performance of transient response due to the changes in the external load. To overcome this problem, a switching algorithm of the control parameter using a learning vector quantization neural network (LVQNN) is proposed in this paper. The LVQNN estimates the external load of the pneumatic artificial muscle manipulator. The effectiveness of the proposed control algorithm is demonstrated through experiments with different external working loads.