http://hdl.handle.net/10453/35202 2026-04-06T23:04:19Z http://hdl.handle.net/10453/102733 Title: ADRC-based model predictive current control for PMSMs fed by three-phase four-switch inverters Authors: Teng, QF; Li, GF; Zhu, JG; Guo, YG; Li, SY Abstract: © 2016 IEEE.A novel automatic disturbances rejection control (ADRC)-based model predictive current control (MPCC) strategy is developed for permanent magnet synchronous motors (PMSMs) fed by three-phase four-switch inverters, an after-fault-topology for fault-tolerant three-phase six-switch inverters. The mathematical model of a PMSM fed by a three-phase four-switch inverter is built firstly. Then the ADRC and MPCC are respectively designed, with the former being used to realize disturbance estimation and disturbance compensation while the latter being used to reduce stator current ripple and improve the quality of the torque and speed control. The resultant ADRC-based MPCC PMSM fed by an unhealthy inverter has fault-tolerant effective with dynamical performance very close to an ADRC-based MPCC PMSM fed by a healthy inverter. On the other hand, compared with PI-based MPCC PMSM fed by an unhealthy inverter, it possesses better dynamical response behavior and stronger robustness as well as smaller THD index of three-phase stator current in the presence of variation of load torque. The simulation results validate the feasibility and effectiveness of the proposed scheme. 2016-07-13T00:00:00Z http://hdl.handle.net/10453/97655 Title: COMPLEX HUMAN AUDITORY PERCEPTION AND SIMULATED SOUND PERFORMANCE PREDICTION Authors: Alambeigi, P; Zhao, S; Burry, J; Qiu, X Editors: Chien, SF; Choo, S; Schnabel, MA; Nakapan, W; Kim, MJ; Roudavski, S 2016-01-01T00:00:00Z http://hdl.handle.net/10453/90329 Title: Selecting the optimal movement subset with different pattern recognition based EMG control algorithms. Authors: Al-Timemy, AH; Khushaba, RN; Escudero, J Abstract: Pattern Recognition (PR)-based EMG controllers of multi-functional upper-limb prostheses have been recently deployed on commercial state-of-the-art prostheses, offering intuitive control with the ability to control large number of movements with fast reaction time. Current challenges with such PR systems include the lack of training and deployment protocols that can help optimize the system's performance based on amputees' needs. Selecting the best subset of movements that each individual amputee can perform will help to exclude movements that have poor performance so that a subject-specific training can be achieved. In this paper, we propose to select the best set of movements that each amputee can perform as well as identifying the movements for which the PR system would have the worst performance and, therefore, would require further training. Unlike previous studies in this direction, different feature extraction and classification methods were utilized to examine if the choice of features/classifiers could affect the best movements subset selection. We performed our experiments on EMG signals collected from four transradial amputees with an accuracy > 97.5% on average across all subjects for the selection of best subset of movements. 2016-08-01T00:00:00Z http://hdl.handle.net/10453/87536 Title: Myoelectric feature extraction using temporal-spatial descriptors for multifunction prosthetic hand control. Authors: Khushaba, RN; Al-Timemy, A; Al-Ani, A; Al-Jumaily, A Abstract: We tackle the challenging problem of myoelectric prosthesis control with an improved feature extraction algorithm. The proposed algorithm correlates a set of spectral moments and their nonlinearly mapped version across the temporal and spatial domains to form accurate descriptors of muscular activity. The main processing step involves the extraction of the Electromyogram (EMG) signal power spectrum characteristics directly from the time-domain for each analysis window, a step to preserve the computational power required for the construction of spectral features. The subsequent analyses involve computing 1) the correlation between the time-domain descriptors extracted from each analysis window and a nonlinearly mapped version of it across the same EMG channel; representing the temporal evolution of the EMG signals, and 2) the correlation between the descriptors extracted from differences of all possible combinations of channels and a nonlinearly mapped version of them, focusing on how the EMG signals from different channels correlates with each other. The proposed Temporal-Spatial Descriptors (TSDs) are validated on EMG data collected from six transradial amputees performing 11 classes of finger movements. Classification results showed significant reductions (at least 8%) in classification error rates compared to other methods. 2016-08-01T00:00:00Z