Advanced Control for Applications最新文献

The severe working conditions of mining machinery make the wear of its parts extremely serious, thus reducing its service life, increasing the probability of accidents, and may cause huge economic wastage. So promoting the improvement of mining machinery is imperative, and the melting of wear-resistant layer on the surface of wear-prone equipment is a more effective improvement method. The study was performed by laser fusing a composite coating of CoCrFeNiMn high-entropy alloy with different WC contents, and a second phase was generated in the coating by an additive method to achieve the strengthening of the coating. The experiments revealed that all the coatings had FCC phase as the main phase by analyzing the phase conformation, structure, stiffness, and abrasiveness. When the WC dosage exceeded 20 wt.%, Fe₃W₃ (M₆C) carbide reinforced phases with different morphologies were produced in the coatings. Forty wt.% of the coatings showed the highest hardness, which was 3.1 times better than that of the coatings without WC particles, and 30 wt.% of the coatings showed the best abrasiveness. HT0 coatings showed the least friction factor of 0.27. HT600 and HT800 corresponded to friction factors of 0.37 and 0.35, respectively. The coefficient of friction of the coating was most stable after about 3 min at room temperature, and the wear phase took longer after annealing. During the break-in phase, the precipitated phase has a supportive effect and prolongs the break-in period. The coating using the studied method improves the wear resistance of mechanical parts and extends their service life, which has some economic and practical value.

Predicting soil moisture accurately is the precondition of realizing accurate irrigation and improving the utilization rate of water resource and the necessary step of developing water-saving agriculture, which can alleviate the water shortage in our agricultural effectively. In order to further improve the accuracy of soil water content prediction, a combined soil water content prediction model based on Autoregressive moving average model (ARIMA model) and back propagation neural network (BP neural network) neural network is proposed. The model considers the linear and nonlinear characteristics of soil water content data, combines them according to the characteristics of the model itself, gives full play to the advantages of ARIMA model and BP neural network. At the same time, two data smoothing methods were used to establish the ARIMA model, and the adaptive moment estimation algorithm (Adam algorithm) and mind evolutionary algorithm (MEA) optimization BP neural network model were used to propose an improved combined prediction model to predict soil water content data. The experimental results show that the average relative error of the improved combinatorial prediction model is 1.51%, which is 4.18%, 0.95% and 3.1% lower than the combinatorial prediction model, BP neural network model and ARIMA model, respectively, and the overall prediction effect is better, which can be used to save agricultural water and provide a strong basis for the development of water-saving agriculture in China. At the same time, it can also ensure that crop production is increased and the purpose of national food security is guaranteed.

This article performs a novel hardware test application on the min–max algorithm to control a two-axis turbofan engine's fuel with a high bypass ratio. In this technique the microcontroller uses a nonlinear model based on the min–max algorithm to control the turbofan's fuel consumption. The min–max control method precisely provides the desired thrust while meeting the engine's physical and operational limitations. By setting the engine's limits appropriately, a surge is prevented from happening due to overheating of the turbine. As a proof of concept, the proposed fuel control algorithm is verified using an Intel Addison's Arduino microcontroller connected to a computer. The implemented hardware is examined by incorporating it into a typical control loop test and monitored via a computer. The achieved results indicate fast time response and algorithm flexibility in simulation modes. The test results confirm the precision and proper implementation of the proposed min–max control algorithm. In addition, the suggested min–max control algorithm can be applied to realize restrictions such as the rotational speed and the outlet pressure of the high-pressure compressor under the required conditions.

In this study, a novel autonomous sensing method of multi-degree-of-freedom industrial robot arm trajectory is proposed. The research takes the distance sensor to collect environmental data, and takes the point cloud data scanned by 3D laser as the basis. The environment model of multi-degree-of-freedom industrial robotic arm is established by Iterative Closest Point (ICP). Then the target object is calibrated by binocular imaging technology. Subsequently, angle of each joint of multi-degree-of-freedom industrial robotic arm is calculated to determine the spatial attitude of the robotic arm. In addition, 3D LiDAR is installed at the end of the robotic arm, and the end trajectory of multi-degree-of-freedom industrial robotic arm is sensed autonomously by using the optimal function. The proposed method has advantages of high accuracy and short sensing time in autonomous sensing of multi-degree-of-freedom industrial robot arm trajectory.

The torque ripple of an induction motor has strong harmonic interference, which causes stator current distortion. Stability control is always a difficult problem in this field. An optimal control method for the torque ripple of induction motors based on harmonic components is proposed. To address the instability and significant energy loss caused by torque ripple in induction motors, an optimization control method for torque ripple in induction motors based on harmonic components is studied and designed. On the basis of the induction motor model, according to the characteristics and generation principle of torque ripple, combined with controllable and uncontrollable parts of harmonic torque, the stator current harmonics are used to suppress torque harmonics and minimize torque ripple. For the purpose of to cancel the harmonic component of the output voltage, achieve digital output voltage filtering, reduce the harmonic component of the stator current, and achieve optimal control of the torque ripple of the induction motor, the resonant digital filter is simultaneously deployed at the controller's output. The test results showed that the maximum torque ripple value was 2 Nm and the minimum torque ripple value was 0.1 Nm, which can restrain the distortion of stator current to the greatest extent and reduce the harmonic content of torque. In summary, the optimization control method proposed in the study can effectively suppress harmonic interference, which has important practical implications for future harmonic research.

To explore the independent influence of the main factors affecting heating on the radiant floor heating system, CFD was used to simulate the simplified multi-storey multi-family building model under different working conditions, and focusing on the calculation and comparative analysis of these four factors (heating supply, the location of room, outdoor temperature and the neighboring heating situation). The results showed that heating supply is the most important factor affecting indoor temperature, and the change of heating supply has the most obvious effect on the change of indoor temperature in the middle of the bottom layer, and the least effect on the edge of the top layer, which is a 1.6–1.7 times relationship. The influence of outdoor temperature on indoor temperature mainly depends on the area proportion of external envelope structure, and the changing rates of the indoor temperature of the edge of the top layer, the middle of the top layer, the edge of the bottom layer, the middle of the bottom layer with the outdoor temperature are 0.94, 0.93, 0.88, and 0.86. The influence of the neighboring heating situation on indoor temperature mainly depends on the surface area proportion of inner envelope structure, and the proportion of heat gain (dissipation) of each envelope to the total heat supply is basically the same, the heat dispersed through the north wall, south wall, east wall and west wall of the room at the middle of the bottom layer account for about 34%, 37%, 9%, and 9% respectively.

An early warning method based on Session Initiation Protocol (SIP) concept for generator set electrical control abnormal response is proposed. The first is to clean the electrical data of the generator set by SIP. Using Supervisory Control and Data Acquisition (SCADA) and Generalized Linear Models (GLM) algorithm, a linear model is constructed to analyze the electrical control of generating units. Using the Autoregressive Integrated Moving Average Model (ARIMA), an abnormal response early warning model for electrical control of generating units is established. BP neural network is used to train the abnormal response data of the generator set electrical control. According to the current response data, the model prediction is realized, and the early warning of the abnormal response of the generator set electrical control is effectively realized. The simulation results show that the proposed method can effectively reduce the early-warning error, false alarm rate, and early-warning delay of generator electrical control abnormal response.

With the development of machine vision technology, visual inspection technology has been widely used in many fields. Among them, the 3D visual detection technology based on line structured light has attracted the attention of relevant scholars because of its advantages of the easy acquisition of image information and high detection accuracy. Therefore, applying machine vision technology to the field of three-dimensional weld inspection can improve the inspection effect, so a machine vision technology based on simulation analysis was proposed. First, the model parameter calibration and algorithm of the structured light imaging system are studied, and then a structured light-based torque converter weld height detection system is designed and related models are constructed. Finally, the detection accuracy and speed of the constructed 3D vision measurement system are verified. The results show that the calibration parameters obtained by the optimization algorithm of this study are as $��t_1�$ follows, focal length = 17.16376; $��u_0�$ = 1798.55439; $��v_0�$ = 1276.85505; $��r_1�$ = 0.14072; $��r_2�$ Secondly, in the comparative experiment, the height of the wave crest coordinates of the measurement results of the three-dimensional detection system of the torque converter weld seam in the global feature extraction process based on the machine vision technology based on simulation analysis is 9.1330 mm, and the maximum error value is 1.0403 mm; As a result of the calibration algorithm, the peak coordinate height is 10.3526 mm, and the maximum error value is 2.7482 mm. The error of the research system is better than Zhang Zhengyou's calibration method. The research content has important reference value for improving the three-dimensional inspection effect of torque converter welds.

The theme of this study is to find a method that can more accurately classify the spatiotemporal distribution characteristics of power system voltage dynamics. As more and more new energy power generation devices are added to the power network, the accuracy of traditional methods for classifying the spatiotemporal distribution characteristics of power system voltage dynamics has gradually decreased. It is significant to reduce the power loss caused by error in identifying voltage dynamic spatiotemporal distribution characteristics. Hence it is necessary to design methods that can more accurately classify power system voltage dynamic spatiotemporal distribution characteristics data. This study proposes a method for studying the spatiotemporal distribution characteristics of power system voltage dynamics based on an improved random forest algorithm. The information entropy calculation method of decision tree in random forest algorithm is improved. According to the experimental results, the classification accuracy of the method for voltage dynamic spatiotemporal distribution characteristics is 99.55%. It can effectively achieve the dynamic spatiotemporal distribution of power system voltage.

This paper conducts in-depth research on the sound source localization technology of the microphone array and designs a sound source localization system based on the cube microphone array. Firstly, the sound source localization model is established using the cube microphone array combined with the spherical near-field acoustic holography. Secondly, the numerical and sound source localization simulations are carried out using the spherical wave. Finally, the simulation and experiment of sound source location for sound at 100, 1000, and 2000 Hz are carried out using the model. Both simulation and experimental results show that when the sound source frequency is 100 and 1000 Hz, the location of the sound source can be accurately located by using the sound source localization model of a cube microphone array, and the sound field reconstruction error is low. When the sound source frequency is 2000 Hz, the location of the sound source cannot be located, and the sound field reconstruction error is very high, which will cause the misjudgment of the sound source location.