Operational Space Iterative Learning Control of Coupled Active/Passive Multilink Cable-Driven Hyper-Redundant Robots

Abstract

The operational space control (OSC) of multilink cable-driven hyper-redundant robots (MCDHRs) are required to perform tasks in many applications. As a new coupled active-passive (CAP) MCDHR system, due to the multiple couplings between the active cables, the passive cables, the joints, and the end-effector, the OSC becomes more and more complicated. However, there is currently no robust and effective control method to solve the OSC problem of such types MCDHRs. In this paper, an OSC framework of CAP-MCDHRs using a dynamics-based iterative-learning-control (ILC) method is proposed, considering multivariate optimization. First, the multi-coupling kinematics and the series-parallel coupling dynamics equation (i.e., cable-joint-end) of the CAP-MCDHR are derived. Then, a dynamics-based trajectory tracking framework was constructed. Moreover, an OSC accuracy evaluation model based on a high-precision laser tracker was also designed. The framework allows the tracking of operational space trajectories (OSTs) online with the feasible cables tension and the joint angle. It is also shown that the tracking performance can be improved through the ILC when the desired trajectory is repeatedly performed. Finally, a simulation and an experimental hardware system is built. The results show that the proposed control framework can be easily and effectively applied to the CAP-MCDHR used in real time.

Screw Dynamics of a Multibody System by a Schoenflies Manipulator

“…The Newton-Euler equation is widely used to establish the dynamics model of a robot. Considering the inertial force and moment, the Newton equations and Euler equations of all rigid bodies can be obtained to solve the closed-loop dynamics of systems such as the Stewart platform [3], a self-reconfiguring chain [4], and multilink cable-driven hyper-redundant robots [5]. The Newton-Euler equations take all joint forces, including the torques and forces, and input and output into account, which is one major advantage of this method.…”