In order to solve the problem of insufficient control performance of various traditional control strategies in the complex environment of grid-connected inverters, the active disturbance rejection control (ADRC) strategy based on the virtual synchronous generator (VSG) is proposed. The mathematical model of a grid-connected photovoltaic inverter based on the VSG is built. The proposed control strategy provides the inverter with more disturbance attenuation and provides rotational inertia. The control strategy estimates and compensates the total disturbance and generates the reference active power and reactive power by ADRC. The control strategy converts the three-phase voltage and current outputs into positive and negative sequences on the dq reference frame. The VSG control module generates a reference voltage command and outputs it to the dual closed loop PI feedforward decoupling control. The PWM signal is finally obtained by the PI feedforward decoupling control. The ADRC strategy based on the VSG does not change the original control characteristics of the VSG; it retains the characteristics of the synchronous generator, and it also provides inertia and damping for the power grid. The simulation shows that the ADRC strategy based on the VSG applied to the inverter can attenuate disturbances. Under the unfavorable conditions of the unstable reference power, such as the unbalanced three-phase voltage and the random disturbance, the output power matches the international electricity standard. INDEX TERMS Virtual synchronous generator (VSG), active disturbance rejection control (ADRC), power control, grid-connected inverter, positive and negative separation. The associate editor coordinating the review of this manuscript and approving it for publication was Huanqing Wang.

In this study, a myoelectrically controlled robotic system with one degree of freedom was developed to assist elbow training in the horizontal plane for patients after stroke. The system could provide assistive extension torque which was proportional to the amplitude of the subject's processed and normalized electromyograhpic (EMG) signal from triceps. The system also provided different resistive torques during movement, which were based on the maximum isometric voluntary extension (MIVE) and flexion (MIVF) torques. A study investigated its effect after 20-session of training for four weeks on the functional improvement of the affected arm in 3 subjects after stroke. Outcome measurements on the muscle strength at the elbow joint showed that there were increases in the MIVE and MIVF torques of the affected arms of all the subjects after the four-week rehabilitation training. The subjects could also reach a more extended position without the assistance of the robotic system than that before the four-week training.

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In this study, a depth camera-based intelligence method is proposed. First, road damage images are collected and transformed into a training set. Then training, defect detection, defect extraction, and classification are performed. In addition, a YOLOv5 is used to create, train, validate, and test the label database. The method does not require a predetermined distance between the measurement target and the sensor; can be applied to moving scenes; and is important for the detection, classification, and quantification of pavement diseases. The results show that the sensor can achieve plane fitting at investigated working distances by means of a deep learning network. In addition, two pavement examples show that the detection method can save a lot of manpower and improve the detection efficiency with certain accuracy.

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