Instead of directly sampling the phase currents, a technique that adopts a single current sensor in the dc link to reconstruct three-phase currents has arisen. Through this approach, the cost of servo systems can be reduced and the reliability can be improved. However, the duration of an effective vector may be so short that the phase current cannot be measured reliably by the conventional space-vector pulsewidth modulation (PWM) algorithm. In this paper, a new phase-current reconstruction method, switching-state phase shift (SSPS), is proposed based on the PWM pattern modification. The modification is performed by applying phase shifts to the switching-state waveforms of the inverter. The algorithm can realize high-quality reconstruction to the phase current with less impacts on the output current ripples and switching losses. Moreover, it can maximize the linear-modulation region by saturation handling. Measurements on the permanent-magnet synchronous motor servo drives show that the SSPS can offer an attractive performance during both steady-state and dynamic operations in the phase-current reconstruction and regulation.

Artificial intelligence provides new feasibilities to the control of dexterous prostheses. To achieve suitable grasps over various objects, a novel computer vision-based classification method assorting objects into different grasp patterns is proposed in this paper. This method can be applied in the autonomous control of the multi-fingered prosthetic hand, as it can help users rapidly complete “reach-and-pick up” tasks on various daily objects with low demand on the myoelectric control. Firstly, a RGB-D image database (121 objects) was established according to four important grasp patterns (cylindrical, spherical, tripod, and lateral). The image samples in the RGB-D dataset were acquired on a large variety of daily objects of different sizes, shapes, postures (16), as well as different illumination conditions (4) and camera positions (4). Then, different inputs and structures of the discrimination model (multilayer CNN) were tested in terms of the classification success rate through cross-validation. Our results showed that depth data play an important role in grasp pattern recognition. The bimodal data (Gray-D) integrating both grayscale and depth information about the objects can improve the classification accuracy acquired from the RGB images (> 10%) effectively. Within the database, the network could achieve the classification with high accuracy (98%); it also has a strong generalization capability on novel samples (93.9 ± 3.0%). We finally applied the method on a dexterous prosthetic hand and tested the whole system on performing the “reach-and-pick up” tasks. The experiments showed that the proposed computer vision-based myoelectric control method (Vision-EMG) can significantly improves the control effectiveness (6.4 s), with comparison to the traditional coding-based myoelectric control method (Coding-EMG, 13 s).