Equilibrium Torque Control-Based Lower-Limb Exoskeleton Assistance With Memory-Enhanced Gait Prediction and Real-Time Learning

The control of lower-limb exoskeletons plays a crucial role in determining the effectiveness of walking assistance, but how to generate a reference signal still poses a significant challenge. Many existing approaches involve offline training and classifiers or depend on prefabricated models, lacking the adaptability needed to support diverse users and real-time scenarios with varying gait cycles. Meanwhile, balancing intervention on human limbs between compliance and assistance during learning is still an open problem. To address these issues, this article proposes a real-time learning method for walking assistance without classifiers, automatically adapting to alterations in motion patterns. The control law, based on adaptive admittance control and the equilibrium state, ensures stable assistance during learning with intuitive parameter tuning and allows for switching of gait during assistance. Utilizing selective memory recursive least squares in a neural network enables rapid learning and precise prediction of human users’ motion intention, without pretraining. Experimental results demonstrate that our approach achieves a prediction error within 6° after half a minute of learning with a prediction ahead time of 120 ms, outperforming classic approaches. The assistance performance is consistent despite varied control parameters, indicating a certain level of robustness.