In the dual‐motor servo system driven by permanent magnet linear synchronous motor, a synchronous control strategy based on coupling parameter identification algorithm and feedback linearization decoupling controller is proposed to overcome the influence of mechanical coupling on the synchronous control performance of the system. Firstly, the mathematical model of synchronous motion system with mechanical coupling dynamics is established. Secondly, a novel coupling parameter identification structure based on disturbance observer is designed. Additionally, the input excitation adopts relatively smooth sinusoidal position and velocity signals to avoid damage to the mechanical structure. Then the feedback linearization method is used to decouple the coupled synchronous control system, and the integrated sliding mode controller is designed for the linear subsystem to improve the robustness of the system. Finally, the experimental results show that the proposed identification method can accurately identify the coupling parameters of the system, and the feedback linearized sliding mode controller based on the identification parameters can effectively eliminate the influence of mechanical coupling and improve the performance of synchronous control.
Anthropometric body dimensions are increasingly recognized as phenotypic markers of obesity risk. Available automated laser measurement systems provide accurate anthropometric dimensions but are costly. The study objective was to develop a low‐cost system that rapidly acquires digital morphology data that can be processed into anthropometric estimates. The core 3D system (KX16; TC2, Cary, NC) includes 16 Kinect (infrared laser projector and monochrome CMOS camera sensor) scanners positioned around the subject; we developed software that matches and composes raw scans and repairs missing/occluded regions to reconstruct complete 3D body geometry. Scan time is ~10 secs and image reconstruction‐digital measurements require 6‐8 min. The 3D system was evaluated in 36 subjects, age 20‐68 yrs and BMI 18.5‐41.4 kg/m2. Digital circumference measurements (mid‐arm; fore‐arm; thigh; ankle) were compared with corresponding tape measurements. The 3D‐ and tape‐measured respective circumferences were highly correlated (R2, 0.89, 0.69, 0.90, and 0.93; all p<0.01) with similar mean (±SD) results (tape/3D: 29.8±4.7 vs. 29.2±4.6; 18.5±2.3 vs. 18.5±2.5; 53.4±6.6 vs. 52.6±6.9; 26.1±4.1 vs. 26.9±4.0 cm) and non‐significant Bland‐Altman plots. Body shape and geometry can thus be quantified with acceptable accuracy using a relatively low‐cost digital imaging system coupled with novel image reconstruction software.
Positioning and tracking are the key technologies of wireless sensor networks. Trilateration is an important method for the localization of wireless sensor network nodes. This method uses three anchor nodes to determine the location of the target. In this paper, UT transform and trilateral measurement method are combined to obtain the statistics of target position, and then use it as the virtual observation of Kalman filter to realize dynamic target tracking. The simulation results verify the performance of the method proposed in this paper.
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