Trajectory tracking of a one-DOF manipulator using multiple fishing line actuators by iterative learning control

In this paper, an iterative learning control scheme for a trajectory tracking task using a one-DOF joint manipulator which is driven by multiple antagonistic fishing line artificial muscle actuators is proposed. The fishing line actuator is one of the soft actuators made by coiling and heating a twisted polymer fiber. It has attracted attention from those who would develop soft robotic devices because it is soft, light, and low-cost. It, however, has several drawbacks, e.g. output force limitation, strong nonlinearity, or energy efficiency, etc. To cope with these drawbacks, firstly a one-DOF manipulator driven by multiple antagonistic actuators is proposed to enhance its output force, and the energy efficiency is analyzed to investigate the relationship between the energy consumption and a number of activated fishing line actuator. Next, an iterative learning control scheme to accomplish a trajectory tracking task by the one-DOF manipulator is proposed to improve its control performance even though under the existence of unknown nonlinearities. The effectiveness of the proposed control scheme is demonstrated through several experiments.