Pneumatic Artificial Muscle Actuators for Compliant Robotic Manipulators

Abstract

Robotic systems are increasingly being utilized in applications that require interaction with humans. In order to enable safe physical human-robot interaction, light weight and compliant manipulation are desirable. These requirements are problematic for many conventional actuation systems, which are often heavy, and typically use high stiffness to achieve high performance, leading to large impact forces upon collision. However, pneumatic artificial muscles (PAMs) are actuators that can satisfy these safety requirements while offering power-to-weight ratios comparable to those of conventional actuators. PAMs are extremely lightweight actuators that produce force in response to pressurization. These muscles demonstrate natural compliance, but have a nonlinear force-contraction profile that complicates modeling and control.

This body of research presents solutions to the challenges associated with the implementation of PAMs as actuators in robotic manipulators, particularly with regard to modeling, design, and control. An existing PAM force balance model was modified to incorporate elliptic end geometry and a hyper-elastic constitutive relationship, dramatically improving predictions of PAM behavior at high contraction. Utilizing this improved model, two proof-of-concept PAM-driven manipulators were designed and constructed; design features included parallel placement of actuators and a tendon-link joint design. Genetic algorithm search heuristics were employed to determine an optimal joint geometry; allowing a manipulator to achieve a desired torque profile while minimizing the required PAM pressure. Performance of the manipulators was evaluated in both simulation and experiment employing various linear and nonlinear control strategies. These included output feedback techniques, such as proportional-integral-derivative (PID) and fuzzy logic, a model-based control for computed torque, and more advanced controllers, such as sliding mode, adaptive sliding mode, and adaptive neural network control. Results demonstrated the benefits of an accurate model in model-based control, and the advantages of adaptive neural network control when a model is unavailable or variations in payload are expected. Lastly, a variable recruitment strategy was applied to a group of parallel muscles actuating a common joint. Increased manipulator efficiency was observed when fewer PAMs were activated, justifying the use of variable recruitment strategies.

Overall, this research demonstrates the benefits of pneumatic artificial muscles as actuators in robotics applications. It demonstrates that PAM-based manipulators can be well-modeled and can achieve high tracking accuracy over a wide range of payloads and inputs while maintaining natural compliance.