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Due to its unique magnetic circuit structure, the synchronous reluctance motor (SynRM) exhibits significant cross-saturation effects during operation, posing challenges for accurate control and performance optimization of the motor. This paper presents a method for identifying cross-saturation characteristic parameters and an improved mathematical modeling method for the nonlinear changes in SynRM parameters caused by magnetic circuit self-saturation and cross-saturation in the static state of the motor. Offline stand still tests are conducted to acquire characteristic data of the motor under different saturation levels. Subsequently, neural networks learn the complex relationships between input currents and output flux linkages, while numerical optimization techniques refine the extracted parameters to minimize model discrepancies. These identified parameters are then integrated into an enhanced mathematical model that effectively incorporates cross-saturation effects. The accuracy and effectiveness of the proposed model are rigorously validated through comprehensive comparisons with finite element analysis (FEA) results and further simulations. [ABSTRACT FROM AUTHOR]

Copyright of Engineering Mechanics / Gongcheng Lixue is the property of Engineering Mechanics 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 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.)

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.)

In order to solve the problem that the current polarization code parameter recognition algorithm lacks the recognition for the starting point of code word and the information bit recognition algorithm is complicated, a polarization code parameter blind recognition algorithm based on the code matrix estimation in proposed. The proposed algorithm firstly multiples the intercepted code word matrix with the Kronecker matrix under the corresponding code length and the reverse rearrangement matrix to obtain the code matrix estimation, then identifies the code length and the code word starting point by the distribution characteristics of the code matrix, and finally uses the trained convolutional neural network to identify the information bits and freezing bits of the polarization code. The experimental results show that the proposed method not only realizes the recognition for the starting point of code word, but also realizes the code length recognition when the code word's starting point is unknown, and the code length recognition accuracy is better than the existing algorithm. When the bit error rate is 0. 19, the polarization code length recognition rate of parameter (32,12) can SÍII reach more than 90%. [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.)

In order to solve the problems of poor automaticity and difficult identification and elimination of spurious modes by covariance-driven stochastic subspace method, a new modal parameter identification method is proposed. Firstly, the covariance - driven stochastic subspace method is used to identify the modal parameters of the system. Secondly, according to the soft and hard criteria, the spurious modes are preliminarily eliminated and the 3D stabilization diagram is drawn. Then, the density-based spatial clustering algorithm with noise (DBSCAN) is improved, the sensitive parameter ε is automatically determined, and the candidate modes are clustered and analyzed. For each cluster of modalities, the modal quality assessment criterion (MQAC) is calculated, and a screening standard is formulated to determine the true modes of the system. Finally, the proposed method is used to verify the modal parameter identification of truss structure, Guangzhou Tower and Z24 bridge examples, The results indicate that this method can achieve autonomous modal analysis of typical engineering structures and effectively eliminate false modes caused by non - white noise excitation and measurement noise. [ABSTRACT FROM AUTHOR]

As an important dynamics parameter, stiffness has a key role in turbopump vibration reduction. Therefore, a prediction model that incorporates attention mechanism and bi-directional long short-term memory (BiLSTM) neural network is proposed. Vibration information is input and the time related historical features are effectively extracted by the LSTM network. Subsequently, the BiLSTM network is built by the inverse superposition of the two-layer LSTM network. This is to accommodate the complex and lengthy sequences of dynamics information, and thus the nonlinear features between parameters are extracted. The weights of the features are obtained by introducing the Attention layer, which will enhance the key information. Finally, the prediction model is trained with turbopump dynamics data. The results show that for turbopump stiffness characteristics, the Attention-BiLSTM model has a significant advantage in sequence data processing, with a mean absolute percentage error (MAPE) of 2.194 5%. In contrast, the MAPEs of RNN, LSTM, and BiLSTM models are 10.497 7%, 5.4973%, and 2.7986%, respectively. It can be seen that the method effectively avoids the complex dynamical inverse problem solving and achieves the dynamic identification of nonlinear parameters. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Applied Mechanics is the property of Chinese Journal of Applied Mechanics 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 Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering 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.)

Copyright of Engineering Mechanics / Gongcheng Lixue is the property of Engineering Mechanics 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 Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment 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 Journal of Guangxi Normal University - Natural Science Edition is the property of Gai Kan Bian Wei Hui 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 Henan University of Technology Natural Science Edition is the property of Henan University of Technology Journal 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.)

Radar direction finding is an essential cooperative monitoring method for aircraft, and the directional parameters of the direction finding antenna determine the accuracy of direction finding. A modified amplitude comparison direction finding model is proposed based on the traditional principle of adjacent amplitude comparison direction finding and the error of amplitude comparison direction finding. Based on the correction of amplitude comparison direction finding model, an error correction genetic simulated annealing (COR-GSA) algorithm is proposed to identify unknown directional antenna directionality parameters by determining the antenna directionality parameters that need to be identified. Identification experiments are conducted using a self-designed dual channel direction finding receiver, and 1 000 sets of aircraft real azimuth data are used for verification. The results show that using the COR-GSA algorithm to identify antenna directional parameters has the highest direction finding accuracy. Finally, the identified antenna directionality parameters are used to track the aircraft's azimuth, and the tracking error is reduced by 18. 3% compared to the tracking error without azimuth correction. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Southeast University / Dongnan Daxue Xuebao is the property of Journal of Southeast University 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.)

The analytical Preisach model solves the problems of large measurement error and numerical instability caused by discrete Everett function in the Preisach model, but there are many parameters and complicated identification problems in the model. In order to solve these problems, an improved chimp optimization algorithm based on multi-strategy fusion was proposed to achieve fast and accurate parameter identification of analytic Preisach model. Firstly, adaptive weighting factors were introduced to balance the global search and local exploitation capabilities. Secondly, the differential variance strategy was applied to update the position of individual populations, to enhance the exchange of information between individual algorithms and expand the search scope. Finally, a stochastic perturbation strategy using a combination of Cauchy and Gaussian variants was used to further enhance the algorithm’ s ability to jump out of local optima. Combined with the experimental data, genetic algorithm, chimpanzee algorithm and the algorithm proposed were used to identify the parameters of the analytical Preisach model. Based on the identification results, the hysteresis loops of the oriented silicon steel sheets were simulated. By comparing the results of hysteresis loop fitting degree, iteration times and fitness value, it can be seen that the proposed algorithm has the advantages of high identification accuracy and fast convergence speed in the identification of parameters of analytic Preisach model. [ABSTRACT FROM AUTHOR]

Copyright of Information & Control is the property of Gai Kan Bian Wei Hui 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.)

When a certain type of aircraft is maneuvered laterally, the aileron and the spoiler will be coupled and deflected to realize the rolling motion of the aircraft. The existing aerodynamic parameter identification methods cannot separately identify the control derivatives of the aileron and the spoiler. Based on the design logic of the aircraft control law and the loading characteristics of the structure during the roll motion, a new method for identifying the aileron control derivative based on the flight measured load is proposed. Firstly, the measurement method of wing aerodynamic load in maneuvering flight is proposed by measuring the structural loads of each load-testing section of the wing in maneuvering flight and combining the structural mass and acceleration distribution outside the section. Secondly, combining the instantaneous rolling moment balance of aircraft lateral control based on the equation and control surface deflection logic, the identification process of aileron control derivatives during roll recovery is deduced, and the identification method of aileron control derivatives based on the measured load of the wing is proposed. Finally, based on the aforementioned method, the identification of aileron control derivatives is carried out, and the influence of different Mach numbers on the derivative of aileron control is analyzed. The results show that the aileron control derivative can be identified based on the flight measured load, and the concentration of the aileron control derivative results is better at the same Mach number. As the Mach number increases, the aileron control effectiveness will decrease. The measured results of flight loads have the potential to be applied to the identification of aerodynamic parameters.

The statistical energy analysis (SEA) method was popularly employed to handle the high-frequency dynamics problems in many engineering fields such as aerospace and shipping. The damping loss factor and coupling loss factor were the major parameters in the SEA theory, and they can usually be identified with the measured external input power and structural modal energy. The measurement errors of the input power and modal energy were not considered in the traditional parameter identification, where the accuracy of the identified results was relatively low. The interval mathematics method was applied to the parameter identification in this study, and the measurement errors of the input power and modal energy were fully considered. The effects of the measurement errors on the parameter identification were revealed, and the accuracy of the identified results was improved. The work can be helpful for the structure design and safety analysis. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering 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.)

Copyright of Pearl River is the property of Pearl River 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 Chinese Journal of Computational Mechanics / Jisuan Lixue Xuebao is the property of Chinese Journal of Computational Mechanics Editorial Office, Dalian 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 South China University of Technology (Natural Science Edition) is the property of South China 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 Electric Drive is the property of Electric Drive 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 Experimental Technology & Management is the property of Experimental Technology & Management 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 Fuzhou University is the property of Journal of Fuzhou University, 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.)

High-speed automatic train operation (ATO) systems inherently exhibit strong nonlinearity and uncertainty. In view of the characteristics of nonlinearity and time-variability of high-speed train model parameters, this paper proposes a feed forward adaptive-generalized model predictive control (FA-GPC) method for dynamic optimization control of the ATO system along with a constrained multi-object predictive controller. Based on a multi-particle train model, an initial analysis explores the impacts of additional resistance changes on train operation. Subsequently, a multi-object performance indicator function containing control input constraints is constructed, combined with key indicators during the operation of high-speed trains, such as speed tracking accuracy, stopping accuracy, and riding comfort. Furthermore, a feed forward generalized prediction speed tracking control algorithm is designed based on the multi-object function, aiming to solve controller overshoot due to additional resistance changes and enhance control convergence rates. Taking into account various factors including influences of external environments and passenger movement during train operation, the resistance changes greatly, making it difficult to establish an accurate mathematical model, a constrained variable forgetting factor-recursive least square method is incorporated to identify the controlled auto-regressive integrated moving average model (CARIMA) of the train control system under different operational conditions. This approach aims to improve the robustness of the control system. Simulation results show that, compared with traditional GPC in the absence of the feed forward functionality and PID controller, the proposed feed forward generalized controller demonstrates good cruise control speed tracking accuracy within a ±0.5 km/h range under different line conditions and strong robustness thanks to the adaptive modification to the feed forward generalized predictive control algorithm for better performance in strong disturbance conditions.

ObjectivesAiming at the challenge of accurate diving modeling of a smart float, an anti-saturation and noise least squares (ASNLS) algorithm is proposed in this paper to achieve diving multi-parameter identification and depth prediction. MethodsFirstly, the nonlinear motion characteristics of the smart float actuator were included in the gray box-based diving model to better fit the actual model, and the continuous diving motion equation was transformed into a discrete form to match the real-world discrete data sampling. Subsequently, the aforementioned discrete diving model was constructed into a correlation form to attenuate the influence of data noise. Finally, by adjusting the values of the covariance matrix, the designed diving parameter identification algorithm achieved resistance to data saturation. ResultsBased on the data of the South China Sea deep diving experiment of the smart float in 2021, diving model parameter identification and depth prediction are carried out. The results demonstrate that the ASNLS algorithm has faster convergence speed (31.8% higher than the least squares algorithm) and smaller depth prediction error (average absolute percentage errors less than 9% at different depths) than both the traditional least squares algorithm and supports the vector machine algorithm.ConclusionsConsequently, the ASNLS algorithm can provide effective support for the depth control and prediction of the smart float.

In response to the issues of a wide variety of equipment, large batches, heavy payloads, limited space, complex assembly paths, and high assembly risks inside the cabin, a heavy-duty positioning and assembly robot was designed. Based on the study of the robot kinematics, an error model was established. With the radius of minimum bounding sphere as the constraint condition, the identification results of error parameters by genetic algorithm were compensated into the robot control system. Taking cabinet assembly as an example, a working path was planned based on spatial constraint conditions. By a dynamic constraint energy consumption function model, multi-objective optimal trajectories were obtained with time, impact, and energy consumption as optimization objectives. Prototype testing verified the effectiveness of the error parameter identification, which reduces the absolute positioning errors of the robot. Moreover, the multi-objective optimal trajectory has a small total joint impact and smooth motion, achieving efficient, smooth, and reliable installation of cabinet-type equipment. [ABSTRACT FROM AUTHOR]

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & 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.)

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China 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 Engineering Mechanics / Gongcheng Lixue is the property of Engineering Mechanics 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 Electric Drive is the property of Electric Drive 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.)

In order to solve the problems of insufficient consideration of the actual efficiency characteristics in the design stage of the hydrostatic transmission system of agricultural and forestry machinery, and the difficulty of parameter identification due to the unknown range of parameter variation in the efficiency characteristics modeling, the matching design, the parameter identification of the efficiency characteristics model and the influence of the reducer transmission ratio were studied.First of all, aiming at the "variable pump-constant motor" power transmission system of a certain type of forest fruit picking machinery, the required power was estimated based on the resistance characteristics of the whole machine when driving on soft soil road, and the variable pump and constant motor models were matched. And then, an improved Monte Carlo method (I-MCM) with adaptive change of decision variables was proposed. According to the bench test data of selected hydrostatic system, the efficiency characteristics model of hydrostatic system was established by parameter identification method. Meanwhile, the model was compared with Monte Carlo method and improved particle swarm optimization algorithm. Finally, based on the established efficiency characteristics model, the influence of the design value of reducer transmission ratio on the energy consumption of the whole machine was analyzed. The results show that I-MCM can still effectively complete parameter identification when the range of parameter variation is uncertain, and the model error after identification is 4.05%. The transmission ratio of the reducer affects the efficiency characteristics of the hydrostatic system in the process of using the whole machine. In the range of the transmission ratio design value, the two are inversely correlated. The average transmission efficiency of the optimal design scheme is 81.45%, which is 10.53% higher than the worst design scheme. The research can provide reference for characteristic evaluation and optimization design of agricultural and forestry machinery or construction machinery. [ABSTRACT FROM AUTHOR]

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.)

Copyright of Acta Scientiarum Naturalium Universitatis Sunyatseni / Zhongshan Daxue Xuebao is the property of Sun-Yat-Sen University 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 Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications 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 Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & 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.)

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.)

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.)

Rubber solid tires are mostly used in mine heavy-duty vehicles, and their mechanical behavior its quite different from that of traditional pneumatic tires. The actual movement status of mine heavy vehicles cannot be accurately described, which severely restricts the study of mine vehicle dynamics and stability due to the lack of accurate tire model parameters. In order to establish an accurate parameter identification model for heavy traction solid tire in mine vehicles, its longitudinal force calculation formula is modified by PAC2002 magic formula model and the parameters to be identified are determined. A hybrid optimization parameter identification algorithm for heavy rubber solid tire classical model is proposed by using a combination of 3 iterative means: Gaussian Newton iteration, genetic iteration and simulated annealing. The basic experimental data of filled rubber tire fitted to a underground coal mine 25 t Heavy-Duty vehicle are acquired through a six component testing equipment, and the parameters of the tire model under two operating conditions of pure longitudinal slip and laterality longitudinal slip compound are discriminated by a hybrid optimization algorithm and a genetic algorithm, and the relative root mean square error and determination coefficient are introduced as evaluation indexes of identification accuracy. The results show that: For the parameter identification model under two working conditions, the maximum root mean square error of the objective function value is 0.08135 and 0.07965 respectively, and the determination coefficients are 0.98815 and 0.98765 respectively. Moreover, the hybrid optimization process has better global control and fast convergence ability than simple genetic iteration. the average algebra convergence to global optimum is reduced by 36% and the average time to global optimum is reduced by 31%; Finally, the vehicle traction characteristics under different load conditions are tested, the test results show that the deviation between the longitudinal traction of a single tire calculated by the identification parameters and the vehicle test results is not more than 4%, and the validity of the identification model is verified.

The creep characteristics of rock are closely related to the long-term stability of rock mass. With the excavation of deep buried tunnels in west China, in order to ensure the safety of engineering and the long-term stability of underground buildings, it is particularly important to study the creep characteristics of rock under complex stress conditions. The traditional creep constitutive model is difficult to accurately describe the accelerated creep stage of rock, and the existing creep model is difficult to fit the creep characteristics of the Pocigou Formation slate. This study takes the Bocigou Formation slate in the Kangding-Xinduqiao section of west Sichuan as the research object. Based on the identification of geological environment background and rock mineral composition, the unloading creep test is carried out. The deformation characteristics of the Bocigou Formation slate under unloading conditions are analyzed, and the creep characteristics of the slate and the damage evolution law during unloading are revealed. Considering the cumulative damage effect in the unloading creep process, the damage variable is introduced to improve the Newtonian element in the traditional Nishihara model, and a creep damage model which can describe the accelerated creep stage is established. The results show that under unloading conditions, the deformation of slate is dominated by instantaneous elastic strain, and the creep phenomenon is significantly enhanced with the increasing deviatoric stress level. The long-term strength of slate is reduced by 20.2%–27.1%. The 1-stOpt is used to identify the parameters of the nonlinear creep damage model. The fitting theoretical curve is in good agreement with the experimental value, and the correlation coefficient reaches 0.945. The improved nonlinear creep damage model after the introduction of damage variable is more reasonable for description of the unloading creep characteristics of slate in the study area, which provides a theoretical basis for the stability analysis of surrounding rock under relevant working conditions.

Dead-beat predictive current control (DPCC) has great potential in the field of motor control due to its fast response. However, it suffers from low robustness due to its sensitivity to parameter variations. In this paper, a DPCC of permanent magnet synchronous motors (PMSM) combined with parameter adaptation is proposed to enhance the robustness of the dead-beat control under parameter misalignment. Firstly, the basic principles of dead-beat current control are introduced. Then, the influence of parameter errors on the control loop of dead-beat current control under imprecise motor parameters is analyzed. To address the issue of imprecise motor parameters, a predictive current control method combined with a parameter-adaptive algorithm is proposed. The motor parameters are identified online, and the parameters of the predictive current controller are modified in real-time to achieve parameter correction. Finally, comparative simulations and performance evaluations are conducted to validate the effectiveness of the proposed method. The results show that the robustness of the current control system is significantly improved, resulting in better control performance when applying the method proposed in this paper.

In order to solve the problem of distributed dynamic characteristic parameter identification of water-lubricated bearings with large length-diameter ratio under partial load, the two-point distributed dynamic model of water-lubricated bearings was established, and the identification method of distributed dynamic characteristic parameters of water-lubricated bearings was proposed. Through simulation test, the accuracy of distributed dynamic characteristic parameter identification of bearings was analyzed by introducing load and displacement signal disturbance, and the distributed dynamic characteristic test of water-lubricated bearings with eccentric load was carried out. The results show that as the excitation force amplitude disturbance increases, the identification error of stiffness and damping coefficients increases linearly. The excitation force phase disturbance has a small impact on the stiffness coefficient, but a significant impact on the damping coefficient. As the displacement amplitude disturbance increases, the identification error of stiffness and damping coefficients increases. The phase disturbance of the displacement signal has a smaller impact on the stiffness coefficient, but a greater impact on the damping coefficient. If the identification error of stiffness and damping is required to be less than 10%,the disturbance amplitude of dynamic load and displacement signal should be less than 10%. If the identification error of stiffness and damping is required to less than 20%,the disturbance phase disturbance deviation of these two signals should be less than 1°. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics 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 China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering 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.)