可拓门控网络驱动的下肢外骨骼智能控制方法研究OA

Lower-limb exoskeleton technology holds substantial promise for applications in rehabilitation medicine and daily mobility assistance.However,achieving adaptive control across complex terrains remains a critical challenge.To address the limited transferability of control strategies and inadequate dynamic adaptability in current systems,this study introduces a mixture-of-experts model that integrates extension gating network with temporal convolutional experts.The model utilizes an extension distance function to dynamically quantify the compatibility between terrain features and ex-pert modules,thereby enabling intelligent routing and adaptive decision-making based on multi-source gait information.By leveraging the temporal modeling capabilities of temporal convolutional experts,the system generates precise assist-ive torques aligned with human motion patterns.In experiments involving multiple terrains including flat ground and slopes,the proposed model reduced the average hip joint torque prediction error by 18.71％compared to conventional LSTM,with an average inference latency of merely 37.09 ms.In scene transition experiments,the model demonstrates a 17.75％reduction in average error compared to LSTM.This model effectively enhances the cross-terrain adaptability of exoskeleton systems,and its extension gating network routing mechanism and information fusion architecture provide valuable insights for multimodal robotic decision-making and dynamic neural network routing optimization.