Iterative Learning Control for Fast and Accurate Position Tracking with an Articulated Soft Robotic Arm
This work addresses the challenge of achieving fast and accurate control for soft robotic arms, which is crucial for applications requiring precise manipulation, though it is incremental as it builds on existing iterative learning control methods.
The paper tackled the problem of improving position tracking for an articulated soft robotic arm during aggressive maneuvers by applying an iterative learning control scheme, resulting in a reduction of the root-mean-square tracking error from 13 degrees to less than 2 degrees in under 30 iterations.
This paper presents the application of an iterative learning control scheme to improve the position tracking performance for an articulated soft robotic arm during aggressive maneuvers. Two antagonistically arranged, inflatable bellows actuate the robotic arm and provide high compliance while enabling fast actuation. Switching valves are used for pressure control of the soft actuators. A norm-optimal iterative learning control scheme based on a linear model of the system is presented and applied in parallel with a feedback controller. The learning scheme 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 13 degrees to less than 2 degrees after applying the learning scheme for less than 30 iterations.