In the lower limb exoskeleton assistance system, motion intention understanding based on biological signals is the human-computer interface’s (HMI) key technology. Due to the close coupling of human and machine, wheelchair-type control modes (combination of motion intentions and switches of different actions) are not applicable. For example, the right/left-hand motor imagery (MI) controls the exoskeleton to move forward/stop. There is a mismatch between MI-based control instructions and actual actions because the forward process alternates between left and right. Therefore, this study aims to design a reasonable HMI for a lower limb exoskeleton assistance system and to prove its rationality and effectiveness. We have designed a new HMI combining the human walking process and multimodal biosignals. Experimental data were collected from 14 subjects. We use feature-based and end-to-end analysis methods to analyze multimode signals. In a feature-based analysis, the advantage of using sEMG to distinguish between different movements does not apply to distinguishing different magnitudes of the same movement. A time-frequency subband selection method based on difference patterns is designed for EEG signals in the feature-based analysis. The fusion of EEG and sEMG is implemented to complement each other. The average recognition rate of the multimodal HMI was evaluated to be up to 89.5%, which is 12% and 7.8% higher than that of EEG and sEMG, respectively. Based on end-to-end analysis, a time-domain translation strategy was used to expand the sample and obtain a cross-subject identification rate of 83.6%. All the results indicate that the designed HMI is rational and effective, and there exists potential for practical application.

In order to enhance prediction precision and tracking performance of current controllers for permanent magnet synchronous motor (PMSM) drives subject to control delay and disturbances, a switching-active-disturbance-rejection-based deadbeatpredictive current control (SADR-DPCC) strategy of the PMSM is presented in the paper. Firstly, traditional DPCC strategy is introduced based on the dynamic model of the PMSM and parameter sensitivity of the DPCC is analyzed. Then an active-disturbance-rejection-based DPCC (ADR-DPCC) strategy is presented to alleviate the influence of disturbances on the DPCC controller. However, the ADR-DPCC only with the linear extended state observer (LESO) is incompetent in the applications with high requirement of control accuracy and robustness. Therefore, the SADR-DPCC strategy of the PMSM is proposed to integrate the advantages of the LESO and nonlinear extended state observer (NLESO) with a hysteretic switching strategy. Moreover, the stability analysis and parameter setting of the NLESO are elaborated, which are simplified by the switching strategy of the SADR-DPCC. Finally, the effectiveness and superiority of the SADR-DPCC are validated experimentally.

To measure the vibration of a target by laser self-mixing interference (SMI), we propose a method that combines feature extraction and random forest (RF) without determining the feedback strength (C). First, the temporal, spectral, and statistical features of the SMI signal are extracted to characterize the original SMI signal. Secondly, these interpretable features are fed into the pretrained RF model to directly predict the amplitude and frequency (A and f) of the vibrating target, recovering the periodic vibration of the target. The results show that the combination of RF and feature extraction yields a fit of more than 0.94 for simple and quick measurement of A and f of unsmooth planar vibrations, regardless of the feedback intensity and the misalignment of the retromirror. Without a complex optical stage, this method can quickly recover arbitrary periodic vibrations from SMI signals without C, which provides a novel method for quickly implementing vibration measurements.