Adaptive Neural Control for Constrained Biomimetic Rehabilitation Robots Using a Novel High-Order Integral Barrier Function

To address the challenges of lumped model uncertainties and tracking error constraints inbiomimetic rehabilitation robot control, this paper proposes a novel high-order integralbarrier function to construct an adaptive neural tracking control scheme. Radial basisfunction neural networks (NNs), inspired by the receptive field mechanism of motorneurons, feature local activation and can accurately approximate the nonlinear dynamicsof such bionic rehabilitation devices. Distinct from traditional integral barrier Lyapunovfunctions, the presented high-order integral barrier function can accommodate both timevaryingand time-invariant error constraints, while simplifying the controller derivationand ensuring full differentiability of virtual control laws throughout the backsteppingframework. Supported by the derived barrier function theorems, the tracking error ofthe robot is theoretically proven to stay within predefined safe boundaries and convergeexponentially to a compact neighborhood of the origin. Finally, comparative numericalsimulations on a biomimetic rehabilitation robot validate the effectiveness of the proposedtheorem and constrained adaptive neural control strategy