Iterative Learning Control for Fast and Accurate Position Tracking with a Soft Robotic Arm
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This paper presents an iterative learning control scheme to improve the position tracking performance for a soft robotic arm during aggressive maneuvers. Two antagonistically arranged, inflatable bellows actuate the robotic arm and provide high compliance while enabling fast actuation. The pressure dynamics of the actuator are derived from first principles and a model of the arm dynamics is determined from system identification. A norm optimal iterative learning control scheme is presented and applied in parallel with a feedback controller. The learning scheme provides monotonic convergence guarantees for the tracking error and is experimentally evaluated on an aggressive trajectory involving set point shifts of 60 degrees within 0.2 seconds. The effectiveness of the learning approach is demonstrated by a reduction of the root-mean-square tracking error from 14 degrees to less than 2 degrees after applying the learning scheme for less than 20 iterations. Finally a method to reduce the sensitivity of the learning approach to non-repetitive disturbances is presented.
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