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Copyright of Journal of National University of Defense Technology / Guofang Keji Daxue Xuebao is the property of NUDT Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the process of torpedo homing guidance, it is difficult for a single guidance method to adapt to different guidance phases and ensure the torpedo guidance effect effectively. For this reason, this paper designs a fuzzy combined guidance law based on the principle of fuzzy control by combining three different typical guidance methods, namely, fixed lead angle guidance method, proportional guidance method, and variable structure guidance method. The results of simulation and comparison in different environments show that the comprehensive performance of the fuzzy combined guidance law is better than that of other typical guidance laws, which can provide reference for the practical application of torpedo homing guidance.

【Objective】 Crops are vulnerable to drought and waterlogging at their seedling stages. Experience-based irrigation and drainage is not effective to alleviate water stresses that seedlings could face. This paper presents an automatic drainage and irrigation system we developed for seedlings of direct-seeded rice. 【Method】 The system includes a micro-sprinkler irrigation system and an automatic drainage device. They are controlled by a LoRa wireless communication network; the irrigation - drainage scheduling was calculated by the fuzzy control algorithm, based on the dynamics of soil moisture and growth of the seedlings measured in the field. The applicability of the system in the fields was evaluated using performance indicators and drainage device performance indicators. 【Result】 ① In the terms of performance, the delay in the response of the system was 0.398 s, the decision-making time was 1.348 s, and the errors of the information monitoring was ±1.48%. ② The automatic drainage device took 4-6 s to close or open, and the standby electric current was 21.7 mA. ③ Experimental results showed that the systems can control soil moisture at 85% of the field capacity 15 days after the seed-drilling; the survival rate of the seedlings was 83.9%. 【Conclusion】 The automatic irrigation and drainage system for seedlings of direct-drilled rice is energy-efficient. It can maintain soil moisture in the proximity of the soil surface around the designed value, alleviating water stress and improving the survival of the seedlings of the directly drilled rice.

In order to improve the control accuracy and robustness of path tracking system for the transplanter, a fuzzy predictive function control method was proposed using feedback linearization in this study. The kinematic model of transplanter was established using Frenet coordinate system. The state feedback method was applied to the nonlinear transplanter system to make the closed loop system become linear system. The Morlet wavelet function was selected as the basis function in predictive function control. The control law of path tracking system for transplanter was designed by the predictive function control algorithm. The weighting coefficient of lateral error in the performance index function for the predictive function control was adjusted online by designing fuzzy rules according to the lateral error and the reference path curvature. The weighting coefficient of change rate of lateral error in the performance index function for the predictive function control was adjusted dynamically by designing fuzzy rules according to the lateral error and the change rate of lateral error. The simulation platform was built for path tracking control of transplanter using Matlab/Simulink software. The simulation results of the fuzzy predictive function control showed that the lateral errors of straight path tracking asymptotically approached zero, and there was no overshoot of actual driving curves at different operating speeds for the straight path tracking. In the straight path tracking, the in-line distance of the fuzzy predictive function control was 1.2, 2.3 and 3.3 m, and the in-line distance of the conventional model predictive control was 2.6, 2.3 and 4.6 m, respectively, when the operating speeds of transplanter were 0.5, 1.0 and 1.5 m/s, respectively. In the case of curve path tracking for the fuzzy predictive function control, the maximum absolute values of lateral error were 0.7, 2.4 and 5.1 cm, and the standard values of lateral error were 0.4, 1.5 and 2.8 cm, respectively, when the operating speeds of transplanter were 0.5, 1.0 and 1.5 m/s, respectively. In the case of curve path tracking for the conventional model predictive control, the maximum absolute values of lateral error were 4.3, 5.5 and 7.8 cm, and the standard values of lateral error were 3.1, 3.5 and 5.0 cm, respectively, when the operating speeds of transplanter were 0.5, 1.0 and 1.5 m/s, respectively. The average operation cycle of the fuzzy predictive function control algorithm was 0.012 s, which was 0.004 s less than that of the conventional model predictive control algorithm. Compared with the conventional model predictive control, the dynamic performance, control accuracy and robustness of path tracking system for transplanter were improved on the premise of ensuring the real-time performance by the fuzzy predictive function control. The automatic driving control system of transplanter was built to install the satellite antenna, satellite receiver, angle sensor, electric steering wheel, controller and vehicle-mounted touch screen on the transplanter. The field experiment was carried out with the automatic driving control system of transplanter. The field test results showed that the fuzzy predictive function control had the strong robustness to the changes of operating speed and reference path curvature. The transplanter tracked the reference path smoothly and effectively. The maximum absolute value of lateral error occurred near the intersection of the straight path and the curve path. Once the operating speeds of transplanter were 0.5, 1.0 and 1.5 m/s, the maximum absolute values of lateral error were 5.9, 7.5 and 9.8 cm, the standard values of lateral error for the straight path were 1.4, 1.7 and 2.7 cm, and the standard values of lateral error for the curve path were 2.5, 3.6 and 5.5 cm, respectively. The fuzzy predictive function control can fully meet the actual control requirements of transplanter, and provided a reference for the research on predictive control method of path tracking for transplanter. [ABSTRACT FROM AUTHOR]

A traditional dredging systems on grab dredgers are typically controlled manually, which leads to low controlprecision and poor dredging flatness. Relying on a control modification project for a specific grab dredger, the operationalcharacteristics of the grab dredger and the driving characteristics of the hydraulic system were analyzed. Adepth -keepingalgorithm based on model predictive control and a fine dredging control algorithm based on fuzzy control were proposed. Adepth-keeping and fine dredging control system based on Siemens PLC was designed, ultimately achieving fully automatic"one -touch fine dredging". Practical application on the dredger demonstrates that the fully automatic operation process ofdepth-keeping and fine dredging control system is stable and reliable, with high control accuracy and flat excavation surfaces,which meets the construction operational requirements and enhances the construction efficiency of grab dredger operations. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power is the property of Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Phalaenopsis has been widely popular ornamental plants, due to its unique appearance and excellent market prospects. The commonly used breeding for phalaenopsis is tissue culture at the same time. However, this task involves high repetition and labor intensity. It is still challenging to increase the production of butterfly orchids. This study aims to improve the yield of tissue culture for the labor-saving in phalaenopsis production. A visual servo platform was proposed with the robotic arm gripping. The picking and planting of tissue-cultured seedlings were realized on the conveyor belt. The visual robotic arm was constructed as a 6-degree-of-freedom robotic arm with eyes on the hand, according to the uncalibrated visual servo control. Firstly, the images were captured from the camera. Image detection was implemented to obtain the image coordinates of the roots, stems, and leaves of phalaenopsis tissue-cultured seedlings. Then, the image data was analyzed to determine the angle between the tissue-cultured seedlings and the end clamp of the robotic arm, as well as the gripping point. According to this angle, the end posture of the robotic arm was obtained during gripping. Afterward, the difference in picking point coordinates was calculated between the current and expected image for the tissue-cultured seedlings. The obtained data was then fed into the fuzzy as an input. The acceleration value was derived to control the speed at the end of the robotic arm from the input quantity. The end gripper of the robotic arm was used to track the tissue-cultured seedlings during planting. The acceleration control was utilized to remain relatively stationary in the planting experiment. The gripping of tissue-cultured seedlings was realized to estimate the posture of the gripper after tracking. The tracking speed was also maintained to plant in the given and fixed culture medium. The accuracy of the original YOLOv8 was improved to identify the roots, particularly for the diverse root morphology of Phalaenopsis tissue-cultured seedlings. AKConv and DSConv modules were introduced to extract the root features, resulting in an improved AKDS_YOLOv8. Compared with the original YOLOv8, the recognition accuracy of AKDS_YOLOv8 increased by 8.6 percentage point for seedling roots, the recall value increased by 10.7 percentage point, and the average accuracy increased by 7.4 percentage point, when the intersection-over-union ratio of the real box and the detection box was set to 50%. The recognition performance was improved significantly. A dual-input single-output fuzzy was utilized by the platform. The input was taken as the difference between the actual and expected pixel coordinates of the target, as well as the variation in this difference. The membership function was used to obtain the membership degrees of different fuzzy sets, corresponding to the difference and the variation in difference. The resulting data was then fed into the fuzzy control rule table. The image deblurring was employed to determine the acceleration at the end of the robotic arm in the direction of the conveyor belt. 80 repeated planting experiments were conducted to verify the success rate of planting. A success rate of 82.5% was achieved under the failure situations. The platform can effectively pick and plant phalaenopsis tissue- cultured seedlings on the conveyor belt, indicating the promising application value. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The speed control of wind turbine blade gluing travel mechanism directly affects the gluing quality and stability. Addressing issues such as inadequate adaptive response, insufficient control precision, and weak resistance to interference during the adhesive application process, an analysis of the overall layout and structural components of the adhesive application equipment was conducted. Subsequently, the mathematical model of state equation controlling voltage input and rack-speed output and the fuzzy proportional-integral-derivative( PID) controller with Kalman-genetic optimization were established. Genetic algorithms were employed to optimize key parameters within the fuzzy controller, thereby achieving precise control of the motion system's speed. The integration of a Kalman filter algorithm was also introduced to mitigate the impact of external disturbances on the system. The walking mechanism prototype was used to verify the fuzzy PID control algorithm optimized by genetic algorithm. The experimental results show that the fuzzy PID control algorithm optimized based on genetic algorithm has an overshoot of 0. 8% and a regulation time of 0. 153 s, which is an improvement compared to both fuzzy PID and PID control algorithms. The fuzzy PID based on Kalman genetic optimization improves the response time by 10 times and reduces interference by 16 times compared to the genetically optimized fuzzy PID. The Kalman genetic algorithm optimized fuzzy PID improves the control accuracy, stability and anti-interference ability of the system, which improves the gluing quality and gluing efficiency of the gluing equipment. [ABSTRACT FROM AUTHOR]

For lithium batteries in fast charging and other scenarios, natural cooling is usually difficult to ensure that the battery temperature is working in the appropriate range, which is very likely to cause the battery temperature is too high or even thermal runaway. The thermal management system integration scheme for different working conditions is proposed, and the vehicle thermal management system model is constructed by AMESim, the compressor speed of the air conditioning system is taken as the regulating quantity, and the compressor speed is regulated by PID control and fuzzy control according to the feedback battery temperature, and the temperature change of the controlled target is analysed by using the high temperature in summer and the intense driving condition US06 as the testing condition. The results show that the designed thermal management integration method and control strategy can meet the cooling requirements of each subsystem, and the fuzzy control can obtain better control effect than PID control. [ABSTRACT FROM AUTHOR]

Copyright of Journal of East China University of Science & Technology is the property of Journal of East China University of Science & Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Slope protection frame construction vehicle is construction machinery equipment for soil slotting and cement concrete sliding form of highway slope. Aiming at the problem of motion control of rotating arm of slope protection frame construction vehicle in the slope environment, a position feedback control strategy based on fuzzy multi-parameter adaptive PID(proportional integral derivative) was designed. In consideration of the difference between the two chambers of the asymmetric hydraulic cylinder, the linear mathematical model of valve controlled asymmetrical hydraulic cylinder was established using area difference equivalent modeling method. By defining fuzzy subset, selecting membership function and establishing fuzzy rules, position control of asymmetrical hydraulic cylinder of rotating arm was realized using fuzzy multi-parameter adaptive PID control algorithm. Simulation and experiments show that fuzzy multiparameter adaptive PID based on the area difference equivalent mathematical model can effectively enhance position tracking control accuracy of hydraulic cylinder, and the effectiveness of the proposed control algorithm was verified. [ABSTRACT FROM AUTHOR]

When the tractor is undertaking the forestry carrying operation, due to the complex road conditions in the forest, it mostly runs at a low speed, has a high steering demand, and often has a load unit, so it pays more attention to its driving stability and steering sensitivity. In this paper, CarSim software is used to build the physical model and load unit model of the tractor, the control strategy of the tractor is designed based on Simulink software combined with the two-degree-of-freedom dynamic model, and the two-degree-of-freedom model of tractor is established. The braking torque feedback control is carried out by combining the yaw rate and the side deflection angle of centroid with the fuzzy control theory. In addition, the proportional control strategy of front and rear wheel angle is introduced according to the four-wheel steering mode of the tractor. Under the CarSim-Simlulink co-simulation platform, combined with the complex road conditions of forestry operations, the low-speed driving and steering experiments of tractor under different control strategies are carried out. The results show that the control strategy adopted in this paper can effectively reduce the steering radius of the tractor under complex road conditions, increase the steering speed, and limit the side deflection angle of centroid to a small range. The tractor has high stability, the steady state value of yaw rate is closer to the ideal state, and the steering is more sensitive. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai University of Engineering Science / Shanghai Gongcheng Jishu Daxue Xuebao is the property of Journal of Shanghai University of Engineering Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Petroleum Machinery is the property of China Petroleum Machinery Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

ObjectivesThe existing hybrid energy storage system control strategy finds it difficult to maintain the state of charge (SOC) within a reasonable range while also meeting the advanced charging and discharging needs due to future wind power fluctuations. Therefore, a new advanced fuzzy control strategy for hybrid energy storage systems was proposed, which takes into account the smoothing of future wind power fluctuations.MethodsFirstly, the wind power needing to be smoothed by different types of energy storage devices was decomposed using the ensemble empirical mode decomposition (EEMD) method. Secondly, the power correction parameter was adjusted according to the SOC and power saturation level of the hybrid energy storage system to correct its output power. Thirdly, the wind power prediction algorithm was used to obtain the predicted value of wind power in the forward-looking cycle. The advance charging and discharging parameters were adjusted to correct the output power of the energy storage system based on the wind power fluctuations in the forward-looking cycle and the over-advance control theory. Finally, taking the actual data of a wind farm as an example, the validity of the proposed forward-looking fuzzy control strategy was validated through simulation.ResultsThe proposed control strategy can not only reduce the over-limit probability of wind power grid-connected fluctuation, significantly reduce the deviation between the total output power and the target power, but also maintain the SOC of the hybrid energy storage system within a reasonable range.ConclusionsThis strategy can provide a useful reference for research related to smoothing wind power fluctuations.

In the process of irrigation and fertilization in agricultural production, the application accuracy of water and fertilizer is not high, resulting in resource waste and soil plate formation. In this study, a fuzzy PID algorithm based on PSO optimization was designed to control the irrigation and fertilization process of the fertilizer applicator. Firstly, the mathematical model of fertilizer applicator was established according to the relevant modules and parameters of fertilization. Based on MATLAB/ Simulink platform, PID controller, fuzzy PID controller and the controller proposed in this paper were built respectively to control the mathematical model of the established system. The simulation results showed that the response time of the proposed control algorithm was reduced to 30 s, which was reduced by 63. 4% and 55. 9% respectively compared with PID and fuzzy PID. In terms of overshoot, there was basically no overshoot in the proposed control algorithm except for the early oscillation. In order to verify the reliability of the algorithm in practical application, this paper designed precision control tests under different five groups of pressures, and the test results showed that the fuzzy PID control based on PSO optimization had good control effect under various pressures. The control accuracy was concentrated in about 2%, while the control accuracy of PID control was about 20%, and the control accuracy of fuzzy PID control was about 10%. It could be seen that the control algorithm proposed in this study could improve the irrigation accuracy in the practical application process. [ABSTRACT FROM AUTHOR]

Compared with the quad rotor UAV (Unmanned Aerial Vehicle), the large twin rotor UAV has the advantages of lower price, longer endurance and higher efficiency, but it has serious coupling and poor anti-in-terference ability. To solve these problems, this study proposes a dual rotor control algorithm using variable universe fuzzy PID (Proportional Integration Differentiation). In this study, the UAV system is divided into position control and attitude control. In attitude control, fuzzy PID is used to adjust the parameters of PID in real time to improve the stability of UAV. According to the motion mode of UAV, a new type of UAV structure is proposed, and the dynamic model of double rotor is established, and the simulation model is built on Simulink for verification. The results show that the proposed algorithm reduces the overshoot by 50% when compared with the ordinary PID control algorithm, which indicates that the proposed algorithm has better stability on large twin rotor UAV and can recover faster in case of interference. [ABSTRACT FROM AUTHOR]

Copyright of Tunnel Construction / Suidao Jianshe (Zhong-Yingwen Ban) is the property of Tunnel Construction Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A fuzzy adaptive control system was presented for greenhouse temperature, according to heat balance and fuzzy control. The accuracy of temperature control was improved to reduce the energy consumption of the control system in the winter greenhouse. An adaptive adjustment module was introduced for the output membership functions using the heat balance equation in the system. The membership functions of output variables were real-time adjusted with the outdoor temperature in the upper computer. The target temperature was ultimately reached to be stable in the greenhouse. At the same time, the experiment was conducted to verify the fuzzy adaptive control. The final experimental results were as follows: (1) The temperature was set to be 30, 35, 40, and 45 ℃ for the water tank of the heating fan. The temperature inside the greenhouse was then monitored for a period of time. The monitoring data was substituted into the energy balance equation to calculate the comprehensive heat transfer coefficient of the heating fan. 19 datasets showed that the comprehensive heat transfer coefficient of the heating fan was 50.50 W/(m2·℃). (2) A fuzzy adaptive control system was developed for greenhouse temperature using Python. A comparison was made on the control effects of fuzzy control, adaptive fuzzy, and threshold control. The more sensitive output response of the fuzzy adaptive control was observed at the target temperature of 20 ℃, according to the heat balance equation. The higher temperature was found in the water tank of heating fan at the beginning of the control. The overall control time was shorter at 35 min. Finally, the temperature of the temperature chamber was stabilized at (19.8 ± 0.11) ℃. There was a longer control of 39 min for the general fuzzy controller. Finally, the greenhouse temperature was stabilized at (13.5 ± 0.5) ℃, which was unable to reach the target ambient temperature. The threshold control had the shortest control time of 26 min. But there were more fluctuations to reach the target temperature until the greenhouse temperature was stabilized at (20.0 ± 0.85) ℃. (3) The ratio of heat input was calculated from the heating fan to the total energy consumption of the greenhouse using different control systems. The energy utilization rate of fuzzy adaptive control was 45.97% when the total energy consumption was 1.584 × 107 J. While the energy utilization rate of threshold control was only 20.21% when the total energy consumption was 3.301 × 107 J. Therefore, low energy consumption, high stability, and accuracy were achieved in the fuzzy adaptive control system with the heat balance equation, fully meeting the needs of temperature control in a winter greenhouse. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Track skid and slip often occur, when the tracking chassis work in soft soil and uneven field. The accuracy of curved path tracking can inevitably deteriorate in automatic navigation. In this work, path tracking was designed to consider the track skid and slip in field conditions. Firstly, the coefficient of turning radius correction was derived to describe the skid and slip characteristics using the kinematic model of the tracked chassis. Then, the lateral and heading deviations were determined by the look-ahead model. Finally, a three-input fuzzy controller was established with the controller inputs of lateral deviation, heading deviation and turning radius correction coefficient. A dual-motor tracked chassis was designed to validate the model. The control hardware included two brushless DC motors, a motor driver, an STM32 microcontroller, and two photoelectric rotary encoders. In navigation, the position, heading and angular velocity of the chassis were measured by a high-precision RTK-GNSS and an MTi-30 inertial sensor. The measurement values were sent to the upper-level computer using a serial port. The path tracking was carried out to calculate the control signal, and then sent into the lower-level microcontroller. The tracking of the reference path was realized by the motor driver of the lower-level microcontroller on both sides. A field experiment was conducted on automatic navigation to verify the performance of a three-input fuzzy controller. The field was selected with a lot of weeds, where the soil was wet and sticky with a moisture content of 22.4%. There was the low flatness with some pits, bulges and hardened soil blocks that scattered throughout the whole field. The speed of the chassis was set to 0.6 m/s in navigation. The look-ahead distance proportional coefficient of the look-ahead model was set to 1.5. An initial lateral deviation of about 1000 cm was set for the correction. The results showed that the average response time of the control system was 6.79 s, while the average distance was 4.08 m. The tracking test of the U-shaped path was performed on the three-input fuzzy controller. The maximum absolute deviation, average absolute deviation and standard deviation of the straight-line and curve tracking section were 12.0 and 21.3 cm, 3.6 and 8.6 cm, as well as 4.4 and 8.4 cm, respectively. The curve tracking with the three-input fuzzy controller was compared with the conventional one, in order to further determine the path tracking accuracy under track skid and slip. The maximum absolute deviation, average absolute deviation and standard deviation of the conventional and three-input fuzzy controller were 31.1 and 22.4 cm, 8.3 and 7.4 cm, as well as 10.2 and 9.1 cm, respectively. Compared with the conventional, the three-input fuzzy controller was improved by 27.95%, 10.84 % and 10.78 %, respectively, in the maximum absolute deviation, average absolute deviation and standard deviation. The better control performance of the navigation system was achieved in the three-input fuzzy controller, indicating the reduced influence of track skid and slip. This finding can also provide a strong reference for the high-precision navigation of tracked chassis in soft soil environments in the field. [ABSTRACT FROM AUTHOR]

In response to the poor disturbance rejection performance and issues of parameter perturbation and control precision in traditional control strategies for interior permanent magnet synchronous motors (IPMSM), a fuzzy sliding mode control strategy based on the super-twisting disturbance observer was proposed. This strategy enables wide-speed-range operation of IPMSM, and further improves rotational speed tracking precision and the system's disturbance rejection capabilities. Firstly,the speed control range was broadened by combining maximum torque per ampere ratio with the gradient descent method for weak magnetic control. Secondly, the super-twisting algorithm (STA) was utilized to design a disturbance observer, thus enhancing the system's ability to resist disturbances. In addition, to address the derivative explosion problem in Backstepping control, a second-order sliding mode differentiator was used to approximate the virtual control rate. The fuzzy logic system was adopted to approximate the nonlinear part in the model of IPMSM, reducing the impact of motor parameter perturbation on control performance. Finally, the stability of the proposed control strategy was confirmed by applying Lyapunov theory. Through simulation on the Matlab/Simulink platform, it was verified that under the control strategy proposed,IPMSM demonstrate superior wide-speed-range dynamic performance and robustness. [ABSTRACT FROM AUTHOR]

Copyright of Tunnel Construction / Suidao Jianshe (Zhong-Yingwen Ban) is the property of Tunnel Construction Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Power Generation Technology is the property of Power Generation Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

ObjectiveThe traditional predictive control approach usually employs a fixed time horizon and often overlooks the impact of changes in curvature and road bends. This oversight leads to subpar tracking performance and inadequate adaptability of robots for navigating curves and paths. Although extending the time horizon of the standard fixed time horizon model predictive control (MPC) can improve curve path tracking accuracy, it comes with high computational costs, making it impractical in situations with restricted computing resources. Consequently, an adaptive time horizon MPC controller was developed to meet the requirements of complex tasks such as autonomous mowing.MethodsInitially, it was crucial to establish a kinematic model for the mowing robot, which required employing Taylor linearization and Euler method discretization techniques to ensure accurate path tracking. The prediction equation for the error model was derived after conducting a comprehensive analysis of the robot's kinematics model employed in mowing. Second, the size of the previewing area was determined by utilizing the speed data and reference path information gathered from the mowing robot. The region located a certain distance ahead of the robot's current position, was identified to as the preview region, enabling a more accurate prediction of the robot's future traveling conditions. Calculations for both the curve factor and curve change factor were carried out within this preview region. The curvature factor represented the initial curvature of the path, while the curvature change factor indicated the extent of curvature variation in this region. These two variables were then fed into a fuzzy controller, which adjusted the prediction time horizon of the MPC. The integration enabled the mowing robot to promptly adjust to changes in the path's curvature, thereby improving its accuracy in tracking the desired trajectory. Additionally, a novel technique for triggering MPC execution was developed to reduce computational load and improve real-time performance. This approach ensured that MPC activation occurred only when needed, rather than at every time step, resulting in reduced computational expenses especially during periods of smooth robot motion where unnecessary computation overhead could be minimized. By meeting kinematic and dynamic constraints, the optimization algorithm successfully identified an optimal control sequence, ultimately enhancing stability and reliability of the control system. Consequently, these set of control algorithms facilitated precise path tracking while considering both kinematic and dynamic limitations in complex environments.Results and DiscussionThe adaptive time-horizon MPC controller effectively limited the maximum absolute heading error and maximum absolute lateral error to within 0.13 rad and 11 cm, respectively, surpassing the performance of the MPC controller in the control group. Moreover, compared to both the first and fourth groups, the adaptive time-horizon MPC controller achieved a remarkable reduction of 75.39% and 57.83% in mean values for lateral error and heading error, respectively (38.38% and 31.84%, respectively). Additionally, it demonstrated superior tracking accuracy as evidenced by its significantly smaller absolute standard deviation of lateral error (0.025 6 m) and course error (0.025 5 rad), outperforming all four fixed time-horizon MPC controllers tested in the study. Furthermore, this adaptive approach ensured precise tracking and control capabilities for the mowing robot while maintaining a remarkably low average solution time of only 0.004 9 s, notably faster than that observed with other control data sets-reducing computational load by approximately 10.9 ms compared to maximum time-horizon MPC.ConclusionsThe experimental results demonstrated that the adaptive time-horizon MPC tracking approach effectively addressed the trade-off between control accuracy and computational complexity encountered in fixed time-horizon MPC. By dynamically adjusting the time horizon length the and performing MPC calculations based on individual events, this approach can more effectively handle scenarios with restricted computational resources, ensuring superior control precision and stability. Furthermore, it achieves a balance between control precision and real-time performance in curve route tracking for mowing robots, offering a more practical and reliable solution for their practical application.

Complicated terrains, specific regions, and high wind turbulence intensity frequently trigger shutdown protection of wind turbines due to vibration beyond the designated limit, resulting in serious loss of power generation. This paper aims at reducing the frequency of shutdowns and improving operational stability of wind turbines. Operational characteristics of wind turbines under complicated conditions such as specific regions and strong turbulent wind were analyzed. A fuzzy control approach was proposed based on pertinent theory, to deal with complicated operating conditions at specific sites. This strategy incorporates a vibration recognition algorithm to classify vibration conditions and a fuzzy control algorithm for vibration suppression based on the categorized conditions. Compared with the conventional shutdown or sector-based control methods, the proposed fuzzy control strategy of vibration suppression eliminates the frequent starting and stopping of wind turbines, thus ensuring power generation and improving operational stability. Furthermore, a simulation analysis was performed using Bladed software, taking 3.x MW wind turbines as the research object. Subsequent experimental verification was carried out at a wind farm. In the experiment, the application of the fuzzy vibration suppression control method effectively led to the quick ride-through of the wind turbine amid the complicated operating conditions including the specific region and strong turbulent wind. A 23.87% reduction in the maximum RMS of the front and rear accelerations of the nacelle demonstrated a substantial enhancement in the wind turbine reliability.

Tracked vehicles have demonstrated superior passability in hilly and mountainous terrains. Among them, the control system of path tracking has been focused mainly on wheeled agricultural equipment rather than tracked vehicles, particularly with single-sided braking tracks. In this study, the fuzzy control of adaptive preview tracking was proposed to improve the parallel control and tracking accuracy with the lower turning count of tracked vehicles. The 3B55 tracked transport vehicle was modified as the experimental platform from Jiangsu Zhushui Agricultural Machinery. A preview tracking model was constructed via the kinematic analysis of single-sided brake-tracked vehicles. Efficient parallel control was achieved in the steering and straight-line travel within the same control cycle. A multiple input/output fuzzy controller was designed with the lateral and heading deviation as the inputs, while the motion distance and turning ratio as the outputs. Furthermore, an adaptive line-of-sight solution was introduced with the improved Sparrow Search algorithm (SSA) to enhance the accuracy of path tracking with less turning count. The chaotic factor and clustering scavengers were also added to the sparrow population, in order to improve the convergence accuracy and susceptibility to local optima. This adaptive Los solution with the improved SSA was analytically determined as the optimal Los, considering the lateral deviation and steering path angle constraints of the vehicle's current state. Simulation and field experiments were carried out to evaluate the tracking accuracy and turning count. Simulation results demonstrate the convergence speed was significantly improved during the early iterations of the improved Sparrow Search. Moreover, the superiority of the improved SSA was validated to successfully escape the local optima with the optimal fitness value after 37 iterations. The on-line modelling test was implemented with the Los distance of 0.8, 1.5, 2, and 3 m. The lateral deviation increased in the same control cycle when the vehicle was reduced the heading deviation. A longer time was required for the vehicle to complete the on-line, with the increase of the Los distance. The accuracy of vehicle tracking decreased gradually, but the number of turning counts was also reduced. An optimal Los distance was obtained in the fuzzy control of adaptive preview tracking in real time, according to the current position and steering path angle of the tracked vehicle during path tracking. When tracking multi-angle planned paths, the turning count was 89 times, with an error area of 1.73 m2. The field experiments were conducted at the Panax Notoginseng Planting Test Field of the Yunnan University Traditional Chinese Medicinal Materials Mechanization Engineering Research Center. The test field was measured as 40 m in length and 6 m in width. The accuracy of path tracking decreased for the tracked transport vehicle in the field, due to the unevenness of the terrain. However, the trends of tracking accuracy and turning count with the Los were consistent with the simulation. Both the turning count and path tracking accuracy decreased gradually, as the driving speed increased, especially in the tracking fuzzy control with fixed Los. When the vehicle tracked at the speeds of 0.14, 0.47, and 0.83 m/s, respectively, the fuzzy control of adaptive preview tracking reduced turning count by 13.59%, 9.87%, and 11.25%, respectively, and the error areas by 19.93%, 48.48%, and 54.59%, respectively. The field and simulation proved that the Adaptive Predictive Tracking Fuzzy Control for Tracked Vehicles can be expected to improve the tracking accuracy of the vehicle and the number of steering control times in the field tracking. The tracking trajectory was closer to the planned path, indicating better performance. This study can provide innovative ideas and technical support to the automatic navigation of agricultural machinery in the single-sided brake-tracked vehicle. [ABSTRACT FROM AUTHOR]

In order to improve the control effect of the traditional AC asynchronous motor vector control system and solve the problems of slow response speed and steady-state jitter caused by the characteristics of AC motor, a fuzzy adaptive vector control system was designed, the traditional vector control system was improved, a four-loop vector control system oriented by the rotor magnetic field was designed, and a fixed value controller was added for the steady-state fluctuation problem of traditional vector control. A simulation system was built in MATLAB/ Simulink, and the rectifier inverter circuit used in AC asynchronous motor was designed to make the experimental results more realistic. The simulation results show that the dynamic performance of the designed four-loop control system is better, faster response, improved load resistance, and has stronger adaptability and robustness. [ABSTRACT FROM AUTHOR]

To analyze the impact of the power system parameter matching design and energy management scheme on the vehicle performance, taking a fuel cell vehicle as research object, the parameters of main power system components were matched. The power-following energy management strategy and the fuzzy control-based energy management strategy were proposed to improve the economic performance of the vehicle, and the sliding average filtering algorithm was used to optimize the output of the fuzzy control strategy. To model the vehicle and energy management system, the joint simulation platform was established based on AVL-Cruise and Simulink to verify the power performance of the vehicle, and the economic performance of the vehicle under three control strategies was comparatively analyzed. The results show that the fuel cell output power curve of the optimized energy management strategy is smoother and is always within the high-efficiency output interval. Based on the principle of fuel cell priority protection, the optimized energy management strategy has the best performance. [ABSTRACT FROM AUTHOR]

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Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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