Position/force evaluation-based assist-as-needed control strategy design for upper limb rehabilitation exoskeleton

In this paper, a position/force evaluation-based assist-as-needed (PFEAAN) control strategy is proposed for an upper limb rehabilitation exoskeleton to stimulate active participation of subject during the training process. The overall control framework contains an outer fuzzy adaptive impedance controller (FAIC) loop to determine the required assistive force and an inner barrier Lyapunov function-based controller with radial basis function neural network estimation (RBFNN-BLFC) loop to control the exoskeleton to provide the desired assistive force, which is obtained from the FAIC. In the outer FAIC loop, an impedance-based assistive force is conduct to assist the subject to achieve accuracy tracking performance. Considering the intervention of the exoskeleton, both position tracking error and assistive force are considered to be the evaluation elements of the subject's motor ability. A fuzzy logic is applied to adjust stiffness parameter based on the subject's actual motor ability. In the inner RBFNN-BLFC loop, a cosecant type barrier Lyapunov function is applied to ensure the exoskeleton tracking error remains bounded within the variable constraint. Besides, RBFNN is used to estimate the uncertain exoskeleton dynamics. Co-simulation studies of Matlab/Simulink and SolidWorks are performed to show the effectiveness of the proposed PFEAAN controller.