Modeling and Control Design for a Musculoskeletal Robot via Adaptive Dynamic Programming

Abstract

In this study, for the musculoskeletal robot system, we aim at optimizing the angle tracking control based on the system model. First, we analyze the driving principle of muscles and establish a bionic muscle dynamics model. Further, based on the geometric relationship between muscles and skeletons, we establish the kinematics model and dynamics model of the musculoskeletal robot system. The adaptive dynamic programming (ADP) algorithm is used for solving the Hamilton-Jacobi-Bellman (HJB) equation. Based on the proposed updating law, the neural network is trained to approximate the solution. Using the Lyapunov’s direct method, we can prove that the system is stable with the proposed controller. Furthermore, simulation indicates that this presented control law is feasible.