Data-Driven Modeling and Simulation of Angle–Torque in a Sensorless Pneumatic Soft Bending Actuator Using the Ideal Gas Law

This paper presents a data-driven modeling and sensorless angle–torque prediction method for a pneumatic soft bending actuator. The actuator contains no embedded angle or torque sensors; instead, only airflow and pressure sensors located in the external control box (standard components in pneumatic systems) are used during operation. The proposed method, and therefore eliminates the need for onboard sensing and detailed valve hysteresis modeling. Based on the ideal gas law, four continuous, monotonic, and single-valued pneumatic state equations were derived and experimentally validated. As a case study, a pneumatic soft actuator was designed to generate high torque for assisting knee and ankle extension. An experimental setup with multiple sensors collected key data on air mass, internal pressure, actuator torque, and bending angle. These additional sensors were used only during dataset generation. A data-driven modeling approach was developed with training neural networks to generate four fitting functions to predict actuator behavior, including equations for angle and torque prediction. An angle-sensorless closed-loop control simulation study, incorporating a PID controller, a proportional valve delay block, and torque prediction, demonstrated the controllability and computational feasibility of the proposed model as well as the actuator’s effectiveness in supporting additional weight during squat-to-stand motion.