Adaptive "load-distributed" muscle coordination method for kinematically redundant musculoskeletal humanoid systems
Muscle force control of musculoskeletal humanoid systems has been studied for years in the motor control, biomechanics and robotics disciplines. However, the study of "load-distributed" muscle control has seldomly been contemplated. In this paper, we consider muscle force control as a problem of muscle coordination. We propose a general muscle coordination method for a system driven by agonist and antagonist muscles. In our method, a set of linear equations is derived by connecting the acceleration description in both joint and muscle space where the pseudo inverse solution to these equations provides an initial optimal muscle force distribution. Thereafter, we redistribute the forces throughout the muscles by deriving a gradient direction for muscle force. This allows the muscles to satisfy force constraints and generate a distribution of forces throughout all the muscles. Moreover, to ensure that our proposed method is adaptive to modeling errors, we have constructed an estimated system model, which is added to the system to represent the real plant. By updating the parameters of the estimated model based on prediction error, the estimated model approaches the real plant gradually in real-time. The overall proposed method is evaluated on a bending-stretching movement of a musculoskeletal arm. We used two models (arm with 6 and 10 muscles) to verify the method. The force distribution analysis verifies the "load-distribution" property of the computed muscle force. The efficiency comparison shows that the computational time does not increase significantly with the increase of muscle number. The tracking error statistics of the two models show the validity of the method. (C) 2014 Elsevier B.V. All rights reserved.