Task performance-based adaptive velocity assist-as-needed control for an upper limb exoskeleton. (March 2022) - Guo, Yida; Wang, Haoping; Tian, Yang; Caldwell, Darwin G.
Highlights: A framework of task performance-based adaptive velocity assist-as-needed (TPAVAAN) control strategy is proposed for upper limb rehabilitation exoskeleton to promote subject active participation in the training process. A position and velocity based double impedance control (PVDIC) is designed to prevent the subject from deviating from the task trajectory and ensure the subject can perform the task at the desired velocity. To evaluate the motor capability of subject, a task performance function is established according to the position tracking error and assistive force, and based on which the desired velocity adaptively is adjusted adaptively. A barrier Lyapunov function-based time-delay estimation controller with neural network compensation (NN-BLFTDEC) is proposed to compensate the exoskeleton dynamics and guarantee the tracking error remain bounded within the constraint. Compared to the other AAN controllers, the effective performance of the proposed control method for assisting subject rehabilitation is validated through the co-simulation studies integrating SolidWorks and Matlab/Simulink. 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 0.013 m and 0.039 m 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 0.025 m/s to 0.118 m/s. The proposed TPAVAAN controller has potential to improve subjects' task performance and modulate the assistance level adaptively.