Speed Fluctuations Research Articles

Vehicle platoon safety considering the vehicle cut-in: A coupled reinforcement learning and model predictive control approach

A longitudinal platoon control method based on Twin Delayed Deep Deterministic Policy Gradient (TD3) and Model Predictive Control (MPC) is proposed to solve the problems of low following efficiency and system instability in longitudinal platoon control. Firstly, Dynamic Bayesian Network (DBN) and Long Short-Term Memory (LSTM) network are introduced to identify the driving behavior of bystanders and derive the MPC objective constraint function containing three indicators of following, comfort and fuel consumption according to the platoon dynamics equation. Secondly, the system prediction model and cost function are introduced into the action-critic network of TD3 to solve the problem of no model training in the traditional TD3 algorithm and to speed up the training speed and accuracy of the network. On this basis, a Bellman equation is proposed to calculate the time-domain difference error and the expected loss function to solve the TD3 network overestimation problem. Finally, the joint simulation platform is built to simulate the platoon driving conditions and compare with the DDPG optimization algorithm and the traditional MPC algorithm respectively. The results show that the improved TD3-MPC algorithm satisfies the constraints and the spacing error is controlled within 0.3 m, and the vehicle speed changes more smoothly in the scenario of speed fluctuation in the front vehicle, and the ride comfort of the platoon is improved, and it has better robustness in the scenario of vehicle cut-in. The experimental results show that when the vehicle speeds are 20, 40, and 60 km/h, compared to MPC, the average spacing errors are reduced by 27.56%, 25.64%, and 28.04%, respectively, and compared to DDPG-MPC, the average spacing errors are reduced by 6.59%, 8.77%, and 7.86%, respectively.

Output speed control for hydro-mechanical continuously variable transmission of tractor.

In order to improve the speed stability of tractors equipped with hydro-mechanical continuously variable transmission (HMCVT) during field operations, the speed regulation characteristics, torque characteristics, and power diversion characteristics of HMCVT are analyzed. And a comprehensive control strategy for HMCVT output speed is proposed by analyzing the factors affecting the fluctuation of output speed, which combines engine speed control and displacement ratio control of hydraulic speed control system. This strategy adopts fuzzy control to regulate the engine speed to maintain the stability of engine operation. Moreover, adopting feed forward compensation control for speed compensation enhances the anti-interference ability of the hydraulic speed control system. And combined with model predictive control, adjust the swash plate swing angle of the variable pump to reduce the fluctuation of HMCVT output speed. By establishing joint simulation model, the effectiveness of the control strategy was verified using tractor acceleration mode and step load disturbance mode. The results show that the strategy can reduce the fluctuation rate of engine speed and impact degree, shorten the adjustment time, and improve the stability of tractor operating speed.

Joint Analysis of Solar Radiation and Wind Speed: Approach With Sliding Windows

Objective: This paper aims to characterise the daily fluctuations of the wind speed and solar radiation time series of some cities in the State of Bahia, Brazil, from January 2009 to December 2018, using the sliding window approach. Theoretical Framework: Due to its complexity and importance for life on planet Earth, climate change and its socio-environmental impacts are subject to studies by the scientific community and governmental and non-governmental institutions. Wind speed and solar radiation are among the elements that are relevant to climate change. Method: The DCCA Cross-Correlation Coefficient (ρDCCA) was applied to meet the objective. Five cities in Bahia, with different biomes, were selected for the modelling. Results and Discussion: The descriptive one with sliding windows identified a predominance of greater relative variation around the mean in the solar radiation time series and divergent signs in the asymmetry of wind speed and solar radiation. It was found in the cross-correlation estimation, via ρDCCA, positive and negative correlations varying according to the city, the size of the window (w) and the evaluated temporal scale (n). Conclusion: From the results and chosen methodology, one more proposal to jointly and dynamically characterise the climatic variables wind speed and solar radiation fluctuations is presented.

Assessment of Numerical Forecasts for Hub-Height Wind Resource Parameters during an Episode of Significant Wind Speed Fluctuations

Assessment of Numerical Forecasts for Hub-Height Wind Resource Parameters during an Episode of Significant Wind Speed Fluctuations

Effect of rotation speed fluctuation on rotor noise generation: A numerical and experimental study

The practical operations of rotors for unmanned aircraft systems can undergo time-varying rotation speeds due to various uncertainty factors. This work investigates the aeroacoustic effect of the rotor’s rotation speed fluctuation using both numerical and experimental approaches. The measured real-time angular speed signal reveals that a hovering rotor experiences highly deterministic rotation speed variations in one revolution, which are closely related to the motor property. Delayed detached eddy simulations are performed, and the fluctuating rotation speed is realized by controlling the mesh displacement at each time step. The results suggest that significant additional tones are produced at the excitation frequency and neighbour frequencies, even though the amplitudes of the rotation speed fluctuations at the dominant frequencies are much smaller than the mean rotation speed. By comparing the full signal to individual harmonic components, it is found that the phase difference between each mode is unimportant. Therefore, the total aeroacoustic effect can be regarded as a linear superposition of different harmonic components. Aeroacoustic source analysis is also performed to isolate the contribution of various source types and correlate the far-field noise with the near-field source distribution. The results show that the extra tonal noise is mostly dominated by the loading noise, with a weaker contribution from the thickness noise. The agreement between the experiments and the simulations indicates that the rotation speed fluctuation is likely responsible for the generation of middle-frequency tonal noise at harmonics of the blade passing frequency.

A Novel Dual Magnetizing Current Controller to Mitigate Speed Fluctuation During Magnetization State Manipulation for Variable Flux Memory Machines

A Novel Dual Magnetizing Current Controller to Mitigate Speed Fluctuation During Magnetization State Manipulation for Variable Flux Memory Machines

Research on Predictive Speed Control Scheme for Surface-Mounted Permanent Magnet Servo Systems

In order to improve the dynamic response and disturbance rejection performance of electric machines, a deadbeat predictive speed control (DPSC) scheme for a permanent magnet synchronous motor (PMSM) is proposed. To begin with, a DPSC controller was proposed with the purpose of achieving precise control for the next control cycle, and the control parameters were determined based on the optimal parameter design method. For better application, performance comparisons were made with a conventional PI control, and the mismatch effects of inertia and torque were analyzed. In order to improve the disturbance rejection performance of the system, an extended sliding mode observer (ESMO) was constructed to compensate for disturbances. Experimental verification with a conventional PI control indicates that the proposed DPSC control can reduce the speed response time from 0.675 s to 0.650 s. When the electric machine operates stably and is applied to a torque disturbance of 0.4 Nm, the speed fluctuation and settling time can be reduced from 9 rpm and 1.7 s to 6 rpm and 0.5 s, respectively. This proposed method effectively enhances the speed control performance of PMSM and can be applied to high-performance electric machine applications.

Space-climatic feedback of the magnetic solar cycle through the interplanetary space

The relationship between solar magnetic activity and solar wind parameters, with observed time-delayed mutual coupling, is an outstanding challenge in space physics. In this study, drawing inspiration from recent observations, we propose a reconciliation framework whose fundamentals stand in the Parker model for solar wind expansion. We investigate the effects on fluctuations in solar wind speed when linearly sustained by an oscillating magnetic solar dynamo described via a modified Van der Pol nonlinear oscillator mimicking the magnetic activity at different timescales. Our findings reveal the presence of a “space-climatic feedback” that, in absence of the driving magnetic activity, slows down solar wind velocity fluctuations. The combined action of the slowing down of fluctuations and a periodic driving is the responsible for the time-delay between solar magnetic activity and solar wind dynamics. Furthermore, we also demonstrate how the space-climatic feedback controls the value of the time-delay which depends on the different periodicities of the driving magnetic activity. This holistic approach provides a formal link at the interplay between solar magnetic activity and solar wind dynamics through the interplanetary space which can advance our understanding of long-term effects of solar activity on solar wind variations, and consequently on interactions with planetary environments.

Resilience Improvement of Microgrid Cluster Systems Based on Two-Stage Robust Optimization

For microgrids in deep and remote sea areas, ocean currents are widely used as an emerging power generation resource. Implementing ES systems is crucial for smooth power output and grid stability. The stability of power output from sea current energy generation faces challenges due to speed fluctuations. Enhancing resilience requires addressing transmission line failures caused by extreme seabed conditions, and ensuring operational security. An ES configuration method considering line faults based on two-stage robust optimization is presented in this paper. First, in order to simultaneously consider planning and operation, a defender–attacker–defender (DAD) model was established. Additionally, the capacity, rated power, and charging/discharging power of ES during operation were jointly optimized through the column-and-constraint generation (C&CG) algorithm. In addition, the rationality and effectiveness of the proposed method were demonstrated through experiments on modified IEEE six-bus and fifty-seven-bus systems. The results show that a distributed ES configuration increased system resilience by 54.60% and reduced abandoned power rate by 57.06% compared with the situation without ES configuration.

Reducing Noise and Impact of High-Frequency Torque Ripple Caused by Injection Voltages by Using Self-Regulating Random Model Algorithm for SynRMs Sensorless Speed Control

For the sensorless control in a low-speed range of synchronous reluctance motors (SynRMs), injecting random high-frequency (HF) square-wave-type voltages has become a widely used and technologically mature method. It can solve the noise problem of traditional injection signal methods. However, all injection signal methods will cause problems such as torque ripple, which causes speed fluctuations. This article proposes a self-regulating random model algorithm for the random injection signal method, which includes a quantity adaptive module for adding additional random processes, an evaluation module for evaluating torque deviation degree, and an updated model module that is used to receive signals from the other two modules and complete model changes and output random model elements. The main function of this algorithm is to create a model that updates to suppress the evaluation value deviation based on the evaluation situation and outputs an optimal sequence of random numbers, thereby limiting speed bias always in a small range; this can reduce unnecessary changes in the output value of the speed regulator. The feasibility and effectiveness of the proposed algorithm and control method have been demonstrated in experiments based on a 5-kW synchronous reluctance motor.

Serial gait speed measurements over time and dynamic survival prediction in older adults

BackgroundA one-time gait speed measurement predicts mortality risk. A framework for updating a clinician’s mortality risk perception with new information from each clinic visit is needed. We used joint modeling of longitudinal and survival data for dynamic prediction of mortality risk. MethodsWe fit sex-stratified joint models to 20-meter (bi)annual longitudinal gait speed measured every 6 months and 14-year survival data from the Health, Aging and Body Composition Study allowing for non-linear fluctuations of gait speed and controlling for important covariates such as age, recent hospitalization, blood pressure, obesity, and comorbidities. ResultsParticipants (N = 3048) were 74 years old with gait speed 1.18 m/s. They were 42% Black, and 52% died over 14 years. Higher gait speed of 0.1 m/s was associated with 23% (95% confidence interval or CI = 20–25%) and 25% (CI = 21–28%) reductions in mortality risk in men and women; and a 0.05 m/s annualized slowing (slope) with 31% (CI = 13–51%) increase in men (all p < 0.05), with findings persisting after covariate adjustment. Distant gait speed history over a year prior contributed little for mortality risk prediction with mean change of only 1–2% in 5-year risk. ConclusionThe two most recent gait speeds appear sufficient to consider for mortality risk in the present initial analysis. More frequent gait speeds need to be considered in mortality risk prediction before definitive conclusions supporting real-world application.

Robust optimized weights spectrum: Enhanced interpretable fault feature extraction method by solving frequency fluctuation problem

Machine condition monitoring (MCM) plays a pivotal role in ensuring the reliability, safety, and efficiency of a production and operation system. Fault feature extraction (FFE), as an important step within MCM, aims to filter out interference components (ICs) and extract fault components (FCs) from raw signals. Consequently, it facilitates incipient fault detection and performance degradation assessment. A recently proposed optimized weights spectrum (OWS) provided a data-driven FFE methodology with a strong theoretical foundation, but it was found that the OWS is highly sensitive to a frequency fluctuation problem caused by rotational speed fluctuations and sensor errors, which may result in fake fault signatures. Therefore, a robust optimized weights spectrum (ROWS) is proposed in this paper to solve the frequency fluctuation problem. The ROWS is inspired by an intuitive idea that employs functions with adaptive frequency bandwidths instead of isolated spectral lines to represent frequency components, thus mitigating the impact of the frequency fluctuation problem. To adaptively determine the optimal parameters for these functions and estimate the corresponding ROWS, an alternative optimization strategy is adopted. Then, the proposed ROWS can be obtained when convergence conditions of this strategy are satisfied. Finally, the effectiveness and superiority of the proposed ROWS are verified by three real-world cases, i.e., the proposed ROWS can solve the frequency fluctuation problem and provide robust results for interpretable FFE.

Transient surface pressure of a rectangular cylinder subjected to downburst-like winds

Thunderstorm downbursts are transient in nature and have been responsible for a variety of structural damages in recent years. Currently, the researchers have done several works on the characteristics of downburst wind speed. Nonetheless, rare attention has been placed on the structural aerodynamics characteristics subjected to downburst winds. Based on this, an experimental investigation is performed to reproduce downburst-like winds physically and to study the transient surface pressures (SPs) on a 5:1 rectangular cylinder (RC). The experiment is conducted within a multiple-fan active control wind tunnel (MFACWT) and mainly focuses on simulating the transient characteristics of downburst-like flow, including time-varying mean (TVM) wind speed and nonstationary wind fluctuation. The resulting SPs are measured to understand the influence of transient wind on the aerodynamic behavior of bluff bodies. The spatiotemporal characteristics of the SPs are analyzed using wavelet transform and Priestley's classic spectral theory. The results indicate that the transient nature of the downburst-like flow can be physically reproduced by a MFACWT. The instantaneous pressures of a RC are illustrated by both the turbulence parameters of the transient flow and the flow-separation characteristics. The pressure coefficients normalized by the TVM of the downburst-like winds remain constant, which provides a more appropriate way to estimate the transient gust loading in a quasi-steady manner. Interestingly, the phenomenon of the time-varying phase shift and time-varying correlation of chordwise SPs is observed when the turbulent velocity changes dramatically. In addition, the normalized surface pressure can be regarded as a stationary stochastic process, which provides a significant basis for further establishing the theoretical model of nonstationary gust-loading.

Speed Fluctuation Suppression Control of Super-High-Speed Electric Air Compressors Considering High-Frequency Electromagnetic Excitation

Speed Fluctuation Suppression Control of Super-High-Speed Electric Air Compressors Considering High-Frequency Electromagnetic Excitation

Harmful Effects, Origin, and Control of Banded Structures in Electroplated Fasteners of High‐Carbon Steel

In this study, the harmful effects of a banded structure on the bending fracture of electroplated fasteners are investigated. The origins and ways to control these structures are also explored. The fracture surfaces of the fasteners exhibit stepwise cracks and numerous intergranular facets associated with ductile tearing, which is a characteristic of hydrogen embrittlement. The origin of these stepwise cracks can be traced to the internal banded structures. Therefore, the internal banded structures promote fracturing during the bending test. The internal banded structures in the fasteners can be traced back to the internal cracks in the bloom, and inappropriate soft‐reduction processes lead to internal cracks. Notably, the carbon segregation at the internal cracks is severe. This is attributed to the proximity of the internal cracks to the center of the bloom, causing the enriched liquid in the interdendritic region to be sucked into these internal cracks. The optimal reduction position and amount are determined by employing heat‐transfer and soft‐reduction models. Fluctuations in superheat and casting speed significantly influence this optimal position and reduction amount.

Insights on the Selection of the Coefficient of Variation to Assess Speed Fluctuation in Swimming.

The aim of this study was to compare swimming speed and speed fluctuations in front crawl between swimmers of different performance levels using discrete variables against statistical parametric mapping (SPM). The sample was composed of 34 male swimmers divided into three groups: (i) group #1-recreational swimmers; (ii) group #2-competitive swimmers aged 12 to 14 years; (iii) group #3-competitive swimmers aged 15 to 17 years. Swimming speed and speed fluctuations (calculated based on four different conditions) were used as discrete variables. Using these discrete variables, ANOVA one-way was used to verify differences between groups, and Bonferroni post-hoc correction for pairwise comparison whenever suitable. SPM (with similar statistical tests) was used to analyze the swimming speed and fluctuation as a continuous variable. Overall, both statistical approaches revealed significant differences (p < 0.001) in swimming speed and speed fluctuations. However, as discrete variables (in four different conditions), the speed fluctuation was not able to detect significant differences between groups #2 and #3. Conversely, SPM was more sensitive and did yield significant differences between these two groups. Therefore, researchers and coaches should be aware that the speed fluctuation as a discrete variable may not identify differences in swimming speed fluctuations when the average value between groups is marginal. On the other hand, SPM was more sensitive in analyzing all groups.

Parameter fuzzy rectification for sliding mode control of five-phase permanent magnet synchronous motor speed control system

Introduction: Nowadays, five-phase permanent magnet synchronous motors have been widely used in the industrial and transportation fields, and the existing sliding mode control methods for speed control systems can no longer meet the requirements such as fast response and good stability.Methods: In light of the aforementioned considerations, the study initially employs mathematical modeling to elucidate the five-phase permanent magnet synchronous motor. Secondly, on the basis of proportional-integral-derivative sliding mode control, radial basis function and Takagi-Sugeno-Kang fuzzy model are introduced for parameter identification and optimization and regulation. Finally, a new neural network regulation algorithm and speed control strategy are proposed.Results and Discussion: The experimental results demonstrated that the expected parameter optimization rate of the regulation algorithm can reach 90%, and the overshooting amount under small inertia working condition is only 3%, and the adjustment time is 0.02 s. The new control algorithm can be used to control the motor speed with the lowest speed fluctuation and the fastest recovery time. In addition, when affected by the load torque, the motor speed controlled by the new strategy fluctuated the least, with a speed drop of only 1% and the fastest recovery time of 0.02 s. It exhibited the lowest control error of 3.7% and the lowest overshooting amount of 5.9%.Conclusion: In summary, the suggested approach has the potential to significantly enhance the speed control system’s control performance while maintaining strong resilience and anti-interference capabilities. The method has certain guiding significance for the practical application of five-phase permanent magnet synchronous motor speed control system.

Load torque observation and compensation for permanent magnet synchronous motor based on sliding mode observer

Background Permanent magnet synchronous motors (PMSM) are widely used in various industries. However, in practical applications, the time-varying nature of load torque may lead to speed fluctuations, negatively impacting the motor's control performance and stability. To mitigate these issues, this paper proposes a load torque observation method for PMSMs based on a sliding mode observer. Methods The sliding mode observer is designed to estimate the load torque and convert it into a current, which is fed back as a feedforward compensation to the q-axis current in the current closed-loop control system. The observer's dynamic performance is optimized using a genetic algorithm to minimize the Integral of Time-weighted Absolute Error (ITAE) between the observer and the actual system state. Experimental tests are conducted on a motor torque testing platform. After stabilizing the motor at 800rpm, a sudden torque of 0.5Nm is applied. Results Compared to the situation without load torque compensation, the motor speed fluctuations are reduced by approximately 60% after adding load torque compensation. Conclusions This enhancement improves the system's speed control performance during torque variations and increases the system's robustness and disturbance rejection capability.

An Economic Performance Improving and Analysis for Offshore Wind Farm-Based Islanded Green Hydrogen System

When offshore wind farms are connected to a hydrogen plant with dedicated transmission lines, for example, high-voltage direct current, the fluctuation of wind speed will influence the efficiency of the alkaline electrolyzer and deteriorate the techno-economic performance. To overcome this issue, firstly, an additional heating process is adopted to achieve insulation for the alkaline solution when power generated by wind farms is below the alkaline electrolyzer minimum power threshold, while the alkaline electrolyzer overload feature is used to generate hydrogen when wind power is at its peak. Then, a simplified piecewise model-based alkaline electrolyzer techno-economic analysis model is proposed. The improved economic performance of the islanded green hydrogen system with the proposed operation strategy is verified based on the wind speed data set simulation generated by the Weibull distribution. Lastly, the sensitivity of the total return on investment to wind speed parameters was investigated, and an islanded green hydrogen system capacity allocation based on the proposed analysis model was conducted. The simulation result shows the total energy utilization increased from 62.0768% to 72.5419%, and the return on investment increased from 5.1303%/month to 5.9581%/month when the proposed control strategy is adopted.

A novel model-based Cauchy-Schwarz divergence condition indicator for gears monitoring during fluctuating speed conditions

Gear monitoring and fault diagnosis are vital for preventing accidents and minimizing economic losses in transportation and industrial systems. Traditional methods use vibration sensors and a two-stage analysis approach: preprocessing data to remove noise and extract relevant components, and generating a condition indicator to detect behavioral anomalies in the gears over time.Time synchronous averaging is a notable tool for monitoring gears at constant speeds. Such a tool filters sensor signals and extracts rotation-related components by using statistical measurements as condition indicators. However, it has limitations in scenarios with time-varying sampling rates and fluctuating speeds, where statistical measures may not fully capture changes in system parameters.This article proposes a novel methodology for monitoring gears in multivariate rotordynamical systems under fluctuating speed conditions. The method integrates time synchronous averaging, system identification algorithms, and statistical tools. It generates a time-synchronous average signal considering speed fluctuations, computes a state–space model of gear behavior in healthy states, extracts residual data from a data-driven model, and generates a condition indicator based on the Cauchy–Schwarz divergence.The proposed methodology was evaluated using experimental data from three rotor dynamical setups under different operational conditions. Validation showed its effectiveness, especially under high-load conditions with significant speed fluctuations.