Learning-Based Force Control of Twisted String Actuators Using a Neural Network-Based Inverse Model

Abstract

In this letter, we propose learning-based force control of twisted string actuators (TSAs) using a neural network-based inverse model. A learning-based force controller is designed using the input and output data of TSAs without a dynamic model of TSAs. Furthermore, the neural network-based inverse model is utilized to reduce model errors and handle nonlinearities between the inputs and outputs of the TSAs. The trained neural network-based inverse model is directly implemented as a force controller for the TSAs. Additionally, we propose data collection methods utilizing three types of inputs to improve the performance of the proposed controller. To verify the improved performance resulting from the proposed data collection methods, we compared the performance of the learning-based force controller for each dataset in the TSA hardware. We then selected the dataset with the best performance among the proposed inputs through experiments. Additionally, to verify the performance of the learning-based force controller, a reference force tracking experiment was performed and compared with a proportional-integral-derivative (PID) controller and feedback linearization. The learning-based force controller utilizing the selected input-based dataset demonstrated higher force tracking performance than the other controllers. Consequently, the TSA's learning-based force control demonstrates robust force control performance.