Model-based online learning and adaptive control for a “human-wearable soft robot” integrated system

Soft robots are considered intrinsically safe with regard to human–robot interaction. This has motivated the development and investigation of soft medical robots, such as soft robotic gloves for stroke rehabilitation. However, the output force of conventional purely soft actuators is usually limited. This restricts their application in stroke rehabilitation, which requires a large force and bidirectional movement. In addition, accurate control of soft actuators is difficult owing to the nonlinearity of purely soft actuators. In this study, a soft robotic glove is designed based on a soft-elastic composite actuator (SECA) that integrates an elastic torque compensating layer to increase the output force as well as achieving bidirectional movement. Such a hybrid design also significantly reduces the degree of nonlinearity compared with a purely soft actuator. A model-based online learning and adaptive control algorithm is proposed for the wearable soft robotic glove, taking its interaction environment into account, namely, the human hand/finger. The designed hybrid controller enables the soft robotic glove to adapt to different hand conditions for reference tracking. Experimental results show that satisfactory tracking performance can be achieved on both healthy subjects and stroke subjects (with the tracking root mean square error (RMSE) < 0.05 rad). Meanwhile, the controller can output an actuator–finger model for each individual subject (with the learning error RMSE < 0.06 rad), which provides information on the condition of the finger and, thus, has further potential clinical application.