Long Short Term Memory Model Based Position-Stiffness Control of Antagonistically Driven Twisted-Coiled Polymer Actuators Using Model Predictive Control
Tuan Luong, Kihyeon Kim, Sungwon Seo, Jeongmin Jeon, Chan-Yong Park, Myeongyun Doh, Ja Choon Koo, Hyouk Ryeol Choi, Hyungpil Moon
Abstract
Twisted-coiled polymer actuators (TCA) have many interesting properties that show potentials for making high performance bionic devices. This letter presents a long short term memory (LSTM) model to predict the behavior of an antagonistic joint driven by hybrid TCA bundles made from Spandex and nylon fibers. By using automatic differentiation, which can be done with PyTorch, a linearized discrete state space model can be formulated and then be utilized in model predictive control (MPC). The experimental results show that by combining LSTM and MPC in modeling and control of the TCA, both high prediction performance and control performance can be achieved. It is verified that using the LSTM model, the joint angle and actuator temperatures can be predicted accurately with an avarage steady state error less than 0.1 deg and 0.2 Cel. deg., respectively. The MPC control results show the controller's ability to reach the desired joint angle with an avarage steady state error of 0.21 degrees in set-point regulation and track a sinusoidal waveform at composite frequencies of 0.1 Hz to 0.15 Hz with the steady-state error less than 0.38 degrees while changing joint stiffness.