Force Estimation and Position Control of a Hydraulic Folded Pouch Actuator for Soft Robotics

Abstract

This paper investigates position control and force estimation for a hydraulic folded pouch actuator. First, experimental platforms are designed to characterize the actuator and the results show two key properties: (i) angular hysteresis when the motion direction reverses, and (ii) strong nonlinearity between liquid volume, pressure, and angle. For position control, we explore three strategies: fully open-loop control, observerbased control, and sensor-based closed-loop control with angle feedback. The closed-loop controller employs dynamically tuned PID gains and an MLP feedforward predictor. Under a sinusoidal reference, the closed-loop controller achieves mean absolute error (MAE) = 4.82° and root mean square error (RMSE) = 5.48°. For force estimation, we train both MLP and LSTM models using liquid volume, angle, pressure, and angular rate as features to predict the external force on the actuator. Compared to the MLP, the LSTM incorporates temporal dynamics, which allows it to capture force variations more effectively and generate smoother prediction results. Under dynamic loads, both models capture the applied force, with the LSTM yielding the lower errors (MAE = 0.96 mN·m, RMSE = 1.23 mN·m).