Muscle circumference sensor and model reference-based adaptive impedance control for upper limb assist exoskeleton robot

In this paper, we have addressed two issues for upper limb assist exoskeleton: (1) estimation of human desired motion intention (DMI) using non-biological-based sensors; and (2) compliant control using model reference-based adaptive approach. For non-biological-based DMI estimation, we have employed Muscle Circumference Sensor (MCS) and load cells. MCS measures human elbow joint torque using human arm kinematics, biceps/triceps muscle model, and physiological cross-sectional area of these muscles. So, using MCS, we have measured Biceps/Triceps internal muscle activity and we have tried to reduce it by providing robotic assistance. To extract DMI, we have employed radial basis function neural network (RBFNN). RBFNN uses position, velocity, and human force to estimate DMI which is further tracked by the impedance control law. This algorithm is based on model reference-based adaptive impedance control law which drives the overall assist exoskeleton to the desired reference impedance model, giving required compliance. To highlight the effectiveness, we have compared proposed control algorithm with simple impedance and adaptive impedance control algorithms. Experimental results demonstrate the reduced muscle activity and active compliance for subject wearing the robot.