Non-Model-Based Finite-Time Adaptive Neural Output Feedback Control for an Active Ankle-Foot Orthosis
Lower limb exoskeletons are designed to assist dependent individuals in their daily activities, such as walking, sitting, or standing, and are also intended for use as devices to support neuromuscular rehabilitation. Among lower limb exoskeletons, Active Ankle-Foot Orthoses (AAFOs) show great potential for improving user mobility. However, accurately developing a realistic dynamic model of the AAFO-wearer system for effective control remains a challenging task. This paper proposes a novel finite-time output-feedback non-model-based adaptive control approach for an AAFO. The approach uses two neural networks (NNs): the first one serves as an adaptive controller for position tracking, while the second is used to estimate external torques acting on the AAFO-wearer system. The adaptive law of the first NN relies on the torque error estimated by a Fuzzy Inference System (FIS), while that of the second NN is based on the tracking error. The proposed approach is designed to provide adaptive assistance during walking based on the estimated external torque. The proposed approach does not require any knowledge of the AAFO-wearer system dynamics and is able to adapt to different walking speeds. The performance of the proposed approach, in terms of trajectory tracking accuracy and robustness against external disturbances and parameter uncertainties, was evaluated through simulations and real-time experiments involving five healthy subjects, and then compared to that of baseline controllers from the literature. Note to Practitioners—This paper proposes a robust non-model-based finite-time output-feedback adaptive control approach for an AAFO intended to assist plantarflexion and dorsiflexion movements of the ankle joint in the sagittal plane. Achieving reliable assistance is challenging because the AAFO-wearer dynamics is affected by uncertainties, unmodeled human-robot interactions, and external disturbances such as ground-reaction forces. The approach uses two neural networks (NNs): the first serves as an adaptive controller for position tracking, while the second estimates the external torques acting on the AAFO-wearer system. The adaptive law of the first NN relies on the torque error estimated by a Fuzzy Inference System (FIS), while that of the second NN is based on the tracking error. The proposed approach is designed to adapt to different gait patterns and maintain both tracking accuracy and robustness with respect to uncertainties and external disturbances. It is validated through simulations and real-time experiments, showing improved trajectory tracking and reduced muscular effort. Future work will focus on validation with real patients under different walking environments.