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 in biomimetic rehabilitation robot control, this paper proposes a novel high-order integral barrier function to construct an adaptive neural tracking control scheme. Radial basis function neural networks (NNs), inspired by the receptive field mechanism of motor neurons, feature local activation and can accurately approximate the nonlinear dynamics of such bionic rehabilitation devices. Distinct from traditional integral barrier Lyapunov functions, the presented high-order integral barrier function can accommodate both time-varying and time-invariant error constraints, while simplifying the controller derivation and ensuring full differentiability of virtual control laws throughout the backstepping framework. Supported by the derived barrier function theorems, the tracking error of the robot is theoretically proven to stay within predefined safe boundaries and converge exponentially to a compact neighborhood of the origin. Finally, comparative numerical simulations on a biomimetic rehabilitation robot validate the effectiveness of the proposed theorem and constrained adaptive neural control strategy.