Muscle Force Control of a Kinematically Redundant Bionic Arm with Real-Time Parameter Update

Redundant muscle-driven arms have numerous advantages, such as increased robustness, ability for load distribution, impedance change etc. However, controlling such a muscle-driven arm is a difficult task. This is mainly due to its redundancy, specially when the muscle force is required to follow certain output constraints and fulfill optimization objectives. In this paper, a new method for controlling such muscle-like systems is proposed. By considering both joint and muscle acceleration contributions, a set of linear equations was constructed. Driving muscle activation is thus framed as the only unknown vector. To solve this linear equation set, a pseudo-inverse solution was used. The null space within this solution represents the internal force, which was used to evenly distribute the muscle forces, which is considered as "anti-fatigue" way. Moreover, to make the proposed method adaptive to modeling errors, an estimated system model was added to represent the real model. By updating the parameters of the estimated model based on prediction error, the estimated model approaches the real model gradually in real time. The overall method was tested for the case of a bending-stretching movement. The presented results verify the validity of the method, and illustrate its useful features and advantages.