Development of a 2-DOF Lower Limb Robotic System Driven by Dual Pneumatic Artificial Muscle Actuators with Proportional Valves
Date Issued
2016
Date
2016
Author(s)
Chan, Che-Wei
Abstract
Rehabilitation robots and exoskeletons have increasingly become popular in the field of robotics, since they can not only provide a support for patients with impaired limbs or the elders with difficulty of doing activities for daily living by their own, but also augment the power of able-bodied people. Of all the actuators, pneumatic artificial muscles (PAMs) may be the most promising one due to their inherent compliance, which guarantees safe interactions between the operator and the device. In addition, high power to weight ratio and lightness are also ideal features for the applications of human-friendly devices. However, the nonlinearity is the drawback that is required to mitigate for accurate control. The purpose of this study is to develop a dual-PAMs driving 2-DOF robotic system, following with the research of [1] for our future objective of the lower limb rehabilitation robot. The system structure is similar to a human lower limb. The test rig of the dual-PAMs driving 2-DOF robotic system is composed of upper leg, lower leg, and each leg is equipped with a proportional-valve controlled dual-PAMs to reduce the system weight. Since the PAMs is a highly non-linear actuator, it is hard to control the system and derive mathematical model precisely. Therefore, the system is controlled by the modified model-free self-tuning PID controller based on neural network to compensate the nonlinearity and improve the tracking performance. For the 2-DOF motion of lower limb, kinematics and inverse kinematics are derived. Finally, the experimental results indicate that 2-DOF tracking motion control of lower limb of the dual-PAMs driving 2-DOF robotic system can be achieved by the self-tuning PID controller with acceptable control error.
Subjects
Pneumatic artificial muscles
pneumatic servo system
exoskeleton
2-DOF robotic system
nonlinear self-tuning neural PID controller
Type
thesis
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