This paper presents a novel hydrostatic actuator, which is named as linear-driven electro-hydrostatic actuator (LEHA). In an LEHA, the actuator is driven by a novel collaborative rectification pump (CRP), which incorporates two miniature cylinders and two spool valves. Specifically, the CRP is driven by two linear oscillating motors, which are designed and optimized to generate reciprocating motion at high frequency with adequate stroke. CRP offers a highly novel linear fluid pump with flexibility in bi-directionally driving. In this paper, schematic of LEHA is first presented and its kinematic flow rate equation is derived. Then the design of CRP, linear oscillating motor, as well as the whole LEHA prototype is introduced. Performance of the LEHA is demonstrated through a series of experiments and simulation, and analysis of the results is also included.
Energetic macroscopic representation (EMR) is an effective graphical modeling tool for multiphysical systems, and EMR model clearly illustrates the power flow and interaction between different subcomponents. This paper presents the modeling and control of a novel linear-driven electro-hydrostatic actuator (LEHA) with EMR method. The LEHA is a novel electro-hydrostatic actuation system, and the hydraulic cylinder in LEHA is driven by a novel collaborative rectification pump (CRP), which incorporates two miniature cylinders and two spool valves. EMR model clearly illustrated the powertrain in LEHA and interaction between each components. Based on EMR model, a maximum control structure (MCS) is easily deduced using the action and reaction principle, and then the practicable controller deduced from MCS shows satisfying performance in the simulation.
Although linear motor has vital and potential applications in air compressors, hydraulic pumps, earphones and electric vehicles because of its good reliability, high power density and convenient maintenance, most researchers rarely concentrate on the dynamic performance of the linear oscillating motor with external force loads. It is essential to study the dynamic performance of the linear oscillating motor with accurate and multi-mode force loads. In this paper, a novel linear oscillating loading system is proposed and the loading system structure is depicted. Then, a mathematical model is built to match the simulation analyses of the dynamic performance of the linear oscillating motor with multi-mode external force loads. Moreover, the linear oscillating loading system platform is built and experiments are undertaken to verify the simulation analyses about the dynamic performance and efficiency with respect to different external force loads, and the simulation and experimental results show good agreement and will have promising significance for linear oscillating motor research and applications.
Linear motors have promising application to industrial manufacture because of their direct motion and thrust output. A permanent magnetic linear oscillating synchronous motor (PMLOSM) provides reciprocating motion which can drive a piston pump directly having advantages of high frequency, high reliability, and easy commercial manufacture. Hence, researching the tracking performance of PMLOSM is of great importance to realizing its popularization and application. Traditional PI control cannot fulfill the requirement of high tracking precision, and PMLOSM performance has high phase lag because of high control stiffness. In this paper, an advancing motivation feedforward control (AMFC), which is a combination of advancing motivation signal and PI control signal, is proposed to obtain high tracking precision of PMLOSM. The PMLOSM inserted with AMFC can provide accurate trajectory tracking at a high frequency. Compared with single PI control, AMFC can reduce the phase lag from −18 to −2.7 degrees, which shows great promotion of the tracking precision of PMLOSM. In addition, AMFC will promote the application of PMLOSM to other working conditions needing high frequency reciprocating tracking performance and give PMLOSM greater future prospects.
Soil cadmium (Cd) pollution is a serious environmental problem imperiling food safety and human health. The endophyte Epichloë gansuensis can improve the tolerance of Achnatherum inebrians to Cd stress. However, it is still unknown whether and how the endophyte helps host plants build up a specific bacterial community when challenged by CdCl2. In this study, the responses of the structure and function of bacterial community and root exudates of E+ (E. gansuensis infected) and E− (E. gansuensis uninfected) plants to Cd stress were investigated. Analysis of bacterial community structure indicated that the rhizosphere bacterial community predominated over the root endosphere bacterial community in enhancing the resistance of CdCl2 in a host mediated by E. gansuensis. E+ plant strengthened the interspecific cooperation of rhizosphere bacterial species. Moreover, the analysis of root exudates demonstrated E. gansuensis and increased the contents of organic acids and amino acids under Cd stress, and most root exudates were significantly correlated with rhizosphere bacteria. These results suggested that E. gansuensis employed a specific strategy to recruit distinct rhizosphere bacterial species and relevant functions by affecting root exudates to improve the tolerance of the host to Cd stress. This study provides a firm foundation for the potential application of symbionts in improving phytostabilization efficiency.
As an essential branch of physical layer authentication research, radio frequency identification (RFID) has advantages in achieving lightweight and highly reliable authentication. However, in the Internet of Things (IoT) environment, where a large scale of devices are connected to the network, there is an issue that the difference of the RF fingerprints is less distinct among the same type of devices. To this end, in this paper, we propose an RFID scheme for IoT devices based on long-short term memory and convolutional neural network (LSTM-CNN). This scheme combines the excellent learning ability of LSTM and CNN to perceive the context information and extract the local feature of RF data. Specifically, RF data is first fed into LSTM to obtain long-term dependency features containing temporal information. Then, CNN is designed for secondary feature extraction to enlarge RF differences and further used for device classification. The experiment results on the open RF data set ORACLE indicate that the identification accuracy of the proposed scheme can reach over 99%. Compared with other schemes, the performance is improved by 6%-30%.
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