Bio-inspired control concepts for elastic rotatory joint drives

Recent research in robotics focuses the attention on the control of compliant actuators to improve safety and to make the interaction with humans more natural. Lightweight construction, real elasticity directly integrated into the joint and control of joint compliance seem to play the most important role for improving safety in human-machine interaction. Humans are intrinsically elastic and the Central Nervous System (CNS) takes advantage of the nonlinear muscle properties to modulate joint stiffness through co-contraction of antagonistic muscles. If alterable compliance in robotic systems is desirable, its introduction can be achieved in two fundamentally different ways. The first way is a technical approach based on the idea of impedance control as formulated by Hogan (1985). The second approach is bioinspired and introduces physiological control mechanisms, muscle models and virtual antagonistic actuation into the control system of a robotics joint drive. Recently, biological models for the control of muscles in vertebrates have been developed (Franklin et al., 2008; Yang et al., 2011). Still, the question remains, how a control algorithm, acting on two or even more muscles, can be implemented in a technical joint. With the objective to implement bio-inspired control strategies on a robotic joint drive, in this thesis, musculoskeletal models, biological parameters and bio-inspired control laws are analyzed and tested. A simplified model of the human elbow joint is used to analyze muscle-like actuation and stiffness properties at the joint. Based on recent results related to how the CNS controls antagonistic muscles, a biological control pattern based on reciprocal activation and co-activation is tested for the control of torque and stiffness at the joint. However, a closer analysis of the musculoskeletal parameters reveals that, despite antagonistic co-activation, domains in the joint range of motion might occur for which stiffness variation is limited (low stiffness variability) or even impossible (stiffness nodes).