Task performance-based adaptive velocity assist-as-needed control for an upper limb exoskeleton

Abstract

In this paper, a task performance-based adaptive velocity assist-as-needed (TPAVAAN) controller is developed for an upper limb exoskeleton to stimulate subject’s participation in the rehabilitation process. This controller includes an inner position and velocity based double impedance control (PVDIC) loop to calculate the assistive force, and an outer barrier Lyapunov function-based time-delay estimation controller with neural network compensation (NN-BLFTDEC) to drive the exoskeleton to provide the required assistive force. In the PVDIC loop, the assistive force is determined by a position based impedance controller to encourage subject in following the desired trajectory, and a velocity based impedance controller to assist the subject to perform the task at the desired velocity. A task performance function that considers position tracking error and assistive force is established to assess the subject’s motor capability and adjust the desired velocity. For the inner loop, the NN-BLFTDEC is designed based on a barrier Lyapunov function (BLF) to constrain the tracking error. A time-delay estimation (TDE) method and radial basis function neural network (RBFNN) are applied to estimate uncertain exoskeleton dynamics. Co-simulation studies are performed in SolidWorks and Matlab/Simulink. The mean tracking errors of the subject are 0.013m and 0.039m with and without the developed controller, respectively. Besides, with the enhancement of subject’s motor capability, the value of the performance function decreases from 7.94 to 1.77, while the desired velocity increases from 0.025m/s to 0.118m/s. The proposed TPAVAAN controller has potential to improve subjects’ task performance and modulate the assistance level adaptively.