Impedance learning control for physical human-robot cooperative interaction

In this paper, three challenges often encountered when upper limb rehabilitation robots are integrated with impaired people are addressed. Firstly, estimation of Desired Intended Motion (DIM) of the robot's wearer is achieved. Secondly, robust adaptive impedance control based on the Modified Function Approximation Technique (MFAT) is designed. Lastly, a new Integral Nonsingular Terminal Sliding Mode Control (INTSMC) is suggested. In particular, the integration of INTSMC enriches the system by ensuring continuous performance tracking of system's trajectories, high robustness, fast transient response, finite-time convergence, and chattering reduction. Besides, the MFAT strategy approximates the dynamic model without collecting any prior knowledge of the lower and upper bounds of the dynamic model's individual uncertainties. Furthermore, leveraging Radial Basis Function Neural Network (RBFNN) to link estimated DIM to the adaptive impedance control allows the upper limb robot to easily track the target impedance model. Finally, in efforts to validate the scheme in real-time, controlled experimental cases are conducted using the exoskeleton robot.