This paper presents a delay-suppressed sliding mode observer (SMO) to observe the real-time rotor position of a permanent magnet synchronous machine (PMSM) controlled by vector control (VC) algorithms. Firstly, in order to solve the low pass filter (LPF) delay problem existing in the traditional signum function-based sliding mode observer (SMO), a brand-new hyperbolic function is initially selected as the switching function. Because a hyperbolic function with proper boundary layer is capable of reducing the chattering phenomenon of a SMO, it is not necessary to re-employ LPFs to eliminate the adverse impacts of chattering on the position estimation accuracy. In order to ensure the reachability and stability of the hyperbolic function-based SMO, the observer gain is calculated by the means of a Lyapunov function in this paper. Secondly, to solve the problem of calculation delay caused by digital computation, a current pre-compensation scheme based on dual-sampling strategy in one switching period is proposed. After compensating the calculation delay, the accuracy of position estimation as well as the motor control performance can be improved. Finally, the proposed SMOs with and without delay compensation are verified by both simulation and experiments which are conducted on a three-phase 1.5kW PMSM drive prototype.

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(UoL). He has published 65 papers in IEEE Transactions journals. His research interests include renewable generation, power electronics converters & control, electric vehicle, more electric ship/aircraft, smart energy system and non-destructive test technology. He is the associate editor of IET Renewable Power Generation, IET Intelligent Transport Systems and Power Electronics and Drives. Mingyao Ma (M'11) received the B.Sc. and Ph.D.

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In view of the need to remove empty cells and unqualified seedlings for automatic transplanting of leafy vegetable seedlings, this paper proposes a method to detect the growth parameters of leafy vegetable seedlings by using machine vision technology. This method uses the image processor PV200 to perform image grayscale, threshold segmentation, corrosion, expansion, area division, etc. to obtain the pixel value of the leaf area of the seedling and compare it with the set standard value, which provides guiding information for eliminating empty cells and unqualified seedlings. Lettuce seedlings at 17 days, 20 days, and 22 days of seedling age were used as the test objects, and the growth status and test results of the seedlings were analyzed to determine the optimum seedling age for transplanting. The test results show that there is basically no leaf cross-border between the lettuce seedlings at the age of 17 days, the average pixel area of the leaves is 3771.74, and the detection accuracy rate is 100%; the seedlings at the age of 22 days grow 5–6 leaves, the detection accuracy of unqualified seedlings and qualified seedlings was 62.50% and 88.16%, respectively, and the comprehensive detection accuracy was 85.71%. The comprehensive detection accuracy rate showed a downward trend with the increase of seedling age, mainly due to the partial occlusion between leaves. The transplanting of leafy vegetable seedlings is a sparse transplanting operation, and the seedling spacing increases after transplanting. Therefore, the detection of seedlings in the process of transplanting can greatly improve the recognition accuracy and solve the problem that the leaves of the seedlings in the seedling tray are obscured by each other and affect the detection accuracy. The research results can provide a theoretical basis and design reference for the development of the visual inspection system and the transplanting actuator of the leafy vegetable seedlings transplanting robot.

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