Repetitive Learning Control for Body Caudal Undulation with Soft Sensory Feedback

Abstract

Soft bio-inspired robotics is a growing field of research that seeks to close the gap with animal robustness and adaptability where conventional robots fall short. The embedding of sensors with the capability to discriminate between different body deformation modes is a key technological challenge in soft robotics to enhance robot control - a difficult task for such kinds of systems with high degrees of freedom. The recently conceived Linear Repetitive Learning Estimation Scheme (LRLES) - to be included in the traditional Proportional Integral Derivative (PID) control - is proposed here as a way to compensate for uncertain dynamics on a soft swimming robot, which is actuated with soft pneumatic actuators and equipped with soft sensors providing proprioceptive information pertaining to lateral body caudal bending akin to a goniometer. The proposed controller is derived in detail and experimentally validated, with the experiment consisting of tracking a desired trajectory for bending angle while continuously oscillating with a constant frequency. The results are compared vis a vis those achieved with the traditional PID controller, finding that the PID endowed with the LRLES outperforms the PID controller (though the latter has been separately tuned) and experimentally validating the novel controllers effectiveness, accuracy, and matching speed.