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This paper presents the application of an iterative learning control schemeto improve the position tracking performance for an articulated soft roboticarm during aggressive maneuvers. Two antagonistically arranged, inflatablebellows actuate the robotic arm and provide high compliance while enabling fastactuation. Switching valves are used for pressure control of the softactuators. A norm-optimal iterative learning control scheme based on a linearmodel of the system is presented and applied in parallel with a feedbackcontroller. The learning scheme is experimentally evaluated on an aggressivetrajectory involving set point shifts of 60 degrees within 0.2 seconds. Theeffectiveness of the learning approach is demonstrated by a reduction of theroot-mean-square tracking error from 13 degrees to less than 2 degrees afterapplying the learning scheme for less than 30 iterations.

Iterative Learning Control for Fast and Accurate Position Tracking with an Articulated Soft Robotic Arm