Soft Artificial Muscle With Proprioceptive Feedback: Design, Modeling and Control

Twisted-coiled polymer actuators (TCAs) have been known for many interesting properties such as inherent compliance, high stroke, high power density. However, the current TCAs normally come along with traditional sensing methods such as encoders attached to the link, which limits the application of the soft actuator. This letter presents a biologically inspired soft robotic muscle from TCAs with embedded proprioceptive position feedback. The embedded elongation sensor fabricated from EcoFlex and liquid metal has little hysteresis and is soft, which can help detect the muscle’s displacement. Moreover, the position of the muscle module is controlled by applying an adaptive backstepping sliding mode control algorithm which can provide finite-time convergence, high tracking performance with no singularity, and less chattering. It is verified from the experimental results that the muscle can be controlled with an average steady state error of 0.15 mm and follow a sinusoidal waveform with composite frequencies of 0.01 Hz and 0.03 Hz using natural cooling. The proposed controller has better performance over computed torque method control and adaptive sliding mode control in terms of maximum error, mean steady state error, rising time, peak time, and overshoot. A robotic hand application driven by the proposed artificial module was also demonstrated.