Speed Control Strategy Research Articles

Dynamic Performance Evaluation of a Five-Phases Induction Motor System controlled using Fuzzy Logic Director

The five-phase induction motor drive offers distinct benefits that enhance the motor's torque production capabilities. The proposed fuzzy logic controller is well suited for high-performance five-phase induction motor drives. This paper is discussed about a speed control strategy for a five-phase induction motor drive system using a Fuzzy Logic Controller (FLC). This controller adjusts system parameters through a rule-based fuzzy logic system, mimicking human reasoning for process control. The speed control algorithm employs indirect vector control.

A position-free control strategy in the full speed domain based on an electro-hydraulic pump

The electric plunger hydraulic pump plays a crucial role in hydraulic transmission technology by integrating an electric motor and a hydraulic pump. The main objectives in designing such devices are to improve efficiency, achieve miniaturization, and promote environmental sustainability. This paper introduces a speed control strategy for an electric hydraulic pump that operates across its entire speed range without relying on position sensor feedback. We begin by establishing the overall model of the electric hydraulic pump and motor characteristics under different operating conditions. Next, we employ a high-frequency injection control method to enable the electric hydraulic pump to start at zero-speed or low-speed states. Additionally, the optimized extended Kalman filtering control method enhances the robustness and tracking performance of the electric hydraulic pump in the medium to high-speed range. Finally, considering the motor characteristics under different operating conditions, we achieve sensorless control of the electric hydraulic pump across the full speed range. Our proposed control strategy provides a promising solution for the control of electric plunger hydraulic pumps, with widespread industrial applications.

A dynamic temporal and spatial speed control strategy for partially connected automated vehicles at a signalized arterial

The connected and automated vehicle (CAV) is able to acquire the global intelligence in advance by communicating with other CAVs and roadside units (RSU), thus integrated speed control has the potential of easing the traffic wave, reducing the fuel consumption and emissions. In this paper, a dynamic temporal and spatial speed control framework is proposed to optimize the travel speed of CAVs along the signalized arterial under the mixed traffic flow including CAVs and human driven vehicles (HDVs). A speed control optimization method is proposed to minimize the number of stops of the ego CAV and its follower HDV with considering the signal status and queuing. A secondary speed control method based on the dynamic control areas is introduced in the mentioned framework to guide the CAV to the targeted positions. The corresponding dynamic variable parameter model is then designed to optimize the operational parameters of the corresponding control area to minimize the total fuel consumption of all vehicles under different market penetration rates. Finally, the simulation platform of Urban Mobility (SUMO) is used to test the proposed speed control strategy. The results indicate that the total stop delays are reduced by 60.9 % and saving 6.5 % total fuel consumption under the 30 % penetration rate.

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.

Fractional-based iterative learning-optimal model predictive control of speed induction motor regulation for electric vehicles application

Introduction. A new control strategy based on the combination of optimal model predictive control (OMPC) with fractional iterative learning control (F-ILC) for speed regulation of an induction motor (IM) for electric vehicles (EVs) application is presented. OMPC uses predictive models to optimize speed control actions by considering the dynamic behavior of the IM, when integrated with the F-ILC, the system learns and refines the speed control iteratively based on previous iterations, adapting to the specific characteristics of the IM and improving performance over time. The synergy between OMPC and F-ILC named F-ILC-OMPC enhances the precision and adaptability of speed control for IMs in EVs application, and optimizes the energy efficiency and responsiveness under varying driving conditions. The novelty lies in the conjunction of the OMPC with the ILC-based on the fractional calculus to regulate the speed of IMs, which is original. Purpose. The new control strategy provides increased performance, robustness and adaptability to changing operational conditions. Methods. The mathematical development of a control law that mitigates the disturbance and achieves accurate and efficient speed regulation. The effectiveness of the suggested control strategy was assessed via simulations in MATLAB conducted on an IM system. Results. The results clearly show the benefits of the F-ILC-OMPC methodology in attaining accurate speed control, minimizing steady-state error and enhanced disturbance rejection. Practical value. The main perspective lies in the development of a speed control strategy for IMs for EVs and the establishment of reliable and efficient electrical systems using ILC-OMPC control. This research has the prospect of a subsequent implementation of these results in experimental prototypes. References 24, tables 2, figures 9.

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.

Unveiling the impact of heterogeneous driving behaviors on traffic flow: A mesoscale multi-agent modeling approach

There are fewer simulation studies that comprehensively consider the impact of collision events due to heterogeneous driving behaviour on multi-lane traffic flow. This comprehensive study utilized multi-agent modeling to simulate the driver-vehicle-road system at the mesoscale level, integrating the characteristics of heterogeneous driving behavior. We developed a traffic flow simulation model for a three-lane urban arterial road segment and used rule-based algorithms to simulate vehicle following, lane changing, and collision behavior. A visualization simulation program was developed using NetLogo software to observe and analyze vehicle behavior. The findings revealed that collision events were the leading cause of traffic bottlenecks, impacting the continuity and coherence of traffic flow. Even after the collision event subsided and congestion eased, the average vehicle speed could not fully recover to its original level, reaching only about 70% of the maximum vehicle speed. Aggressive drivers exhibited distinct speed control strategies compared to conservative and ordinary drivers. This study demonstrates the effectiveness of multi-agent modeling in capturing the relationship between traffic bottlenecks and collision events, highlighting the influence of dynamic traffic events on driving behavior.

High production of enantiopure (R,R)-2,3-butanediol from crude glycerol by Klebsiella pneumoniae with an engineered oxidative pathway and a two-stage agitation strategy

Background(R,R)-2,3-butanediol (BDO) is employed in a variety of applications and is gaining prominence due to its unique physicochemical features. The use of glycerol as a carbon source for 2,3-BDO production in Klebsiella pneumoniae has been limited, since 1,3-propanediol (PDO) is generated during glycerol fermentation.ResultsIn this study, the inactivation of the budC gene in K. pneumoniae increased the production rate of (R,R)-2,3-BDO from 21.92 ± 2.10 to 92.05 ± 1.20%. The major isomer form of K. pneumoniae (meso-2,3-BDO) was shifted to (R,R)-2,3-BDO. The purity of (R,R)-2,3-BDO was examined by agitation speed, and 98.54% of (R,R)-2,3-BDO was obtained at 500 rpm. However, as the cultivation period got longer, the purity of (R,R)-2,3-BDO declined. For this problem, a two-step agitation speed control strategy (adjusted from 500 to 400 rpm after 24 h) and over-expression of the dhaD gene involved in (R,R)-2,3-BDO biosynthesis were used. Nevertheless, the purity of (R,R)-2,3-BDO still gradually decreased over time. Finally, when pure glycerol was replaced with crude glycerol, the titer of 89.47 g/L of (R,R)-2,3-BDO (1.69 g/L of meso-2,3-BDO), productivity of 1.24 g/L/h, and yield of 0.35 g/g consumed crude glycerol was achieved while maintaining a purity of 98% or higher.ConclusionsThis study is meaningful in that it demonstrated the highest production and productivity among studies in that produced (R,R)-2,3-BDO with a high purity in Klebsiella sp. strains. In addition, to the best of our knowledge, this is the first study to produce (R,R)-2,3-BDO using glycerol as the sole carbon source.

Rotating speed pulling-back control and adaptive strategy of doubly-fed variable speed pumped storage unit

When the doubly-fed variable speed pumped storage unit (DFVSPSU) adopts the power priority control, the rotating speed may change excessively during the transient process, potentially causing the rotor side power to exceed the converter capacity, which may damage the converter. Therefore, this study aims to analyze the mechanism underlying rotor side power overlimit and conventional rotating speed pulling-back control (RSPBC), proposing an adaptive rotating speed pulling-back control (ARSPBC). First, the unit's speed operating range and the converter capacity were determined based on the characteristic curve of the pump-turbine. Second, the optimal speed and speed limit were compared, indicating that the unit's operation is closer to the lower speed limit, which increases the likelihood of power overlimit occurrences. Third, the mechanism of rotor side power overlimit and conventional RSPBC were analyzed. It was noted that although conventional RSPBC can prevent rotor side power overlimit, it can result in significant generator power fluctuations. Finally, an ARSPBC incorporating speed change rate feedback was proposed. The numerical simulation showed that the ARSPBC can not only prevent the rotor side power overlimit, but also reduce the generator power fluctuations. Compared to the conventional RSPBC, the power fluctuation was reduced from 70 % to 62.5 %–4 %.

Adaptive Speed Control Scheme Based on Congestion Level and Inter-Vehicle Distance

Cellular vehicle-to-everything (C-V2X) enables short-distance communication between vehicles and other users to improve road safety through data sharing. Conventional research on C-V2X typically assumes that vehicles travel at the same speed with a fixed inter-vehicle distance (Disinter). However, this assumption does not reflect the real driving environment or promote road traffic efficiency. Conversely, assigning different speeds to vehicles without a structured approach inevitably increases the collision risk. Therefore, determining appropriate speeds for each vehicle in the C-V2X framework is crucial. To this end, considering the road environment and mobility, this study introduces an adaptive speed mechanism based on the congestion level of a zone and Disinter. First, the given scenario is divided into several zones. Subsequently, based on the congestion level of a zone and the Disinter level, an adaptive speed is defined for each vehicle. This approach ensured that vehicles adopt lower speeds in congested situations to reduce the collision probability and higher speeds in sparse traffic cases to improve traffic efficiency. The performance of the proposed adaptive speed scheme is compared with that of conventional fixed-speed settings. The results show that the adaptive speed control scheme outperforms conventional fixed-speed schemes in terms of the packet reception ratio (PRR) and collision ratio (CR). Specifically, the proposed mechanism can reduce the CR to 0 and ensure that the PRR is higher than 0.98 in low-density scenarios.

New Opportunities in Real-Time Diagnostics of Induction Machines

This manuscript addresses the critical challenges in achieving high-accuracy remote control of electromechanical systems, given their inherent nonlinearities and dynamic complexities. Traditional diagnostics often suffer from data inaccuracies and limitations in analytical techniques. The focus is on enhancing the dynamic model accuracy for remote induction motor control in both closed- and open-loop speed control systems, which is essential for real-time process monitoring. The proposed solution includes real-time measurements of input and output physical quantities to mitigate inaccuracies in traditional diagnostic methods. The manuscript discusses theoretical aspects of nonlinear torque formation in induction drives and introduces a dynamic model employing vector control and speed control schemes alongside standard frequency control methods. These approaches optimize frequency converter settings to enhance system performance under varying nonlinear conditions. Additionally, the manuscript explores methods to analyze dynamic, systematic errors arising from frequency converter inertial properties, thereby improving electromechanical equipment condition diagnostics. By addressing these challenges, the manuscript significantly advances the field, offering a promising future with enhanced dynamic model accuracy, real-time monitoring techniques, and advanced control methods to optimize system reliability and performance.

Research and simulation of high-efficiency disturbance rejection speed control strategy for PMSM

Research and simulation of high-efficiency disturbance rejection speed control strategy for PMSM

Quantifying power regulation characteristics in pump mode of variable‐speed pumped storage unit with DFIM

AbstractPumped storage plants (PSPs) play an important role in renewable energy consumption in power systems. Variable‐speed technology is a new and critical direction for the development of PSPs. In pump mode, variable‐speed pumped storage units (VSPSUs) have wider power regulation ranges and more flexible power responses than fixed‐speed pumped storage units (FSPSUs); however, the corresponding quantification study of VSPSUs is rare. Therefore, this study aims to quantify the static and dynamic power regulation characteristics of VSPSU in pump mode. First, the modeling of the fast speed control strategy of VSPSU in pump mode is improved. Then, the power regulation range of the VSPSU is explored. The factors (i.e., power command, governor, and converter) affecting the power response rapidity are quantified through the engineering case of a variable‐speed PSP adopting the doubly fed induction machine. Accordingly, suggestions on the settings of the parameters are provided. Quantification results show that parameter tuning based on this study effectively solves the problem of low regulation rate caused by great power fluctuations under typical power regulation cases in pump mode. This VSPSU has the fastest regulation rate of 13.75%/s when adjusting 40% power. This study could provide technical support to the practical operation of VSPSU.

A novel full-process test bench for deep-sea in-situ power generation systems

A novel full-process test bench was developed to replace lengthy and costly deep-water experiments. This platform can accurately simulate the dynamic characteristics of hydrokinetic turbines and can be used to evaluate their power-supply capabilities under various conditions. The hydrokinetic turbine power model was established by obtaining discrete performance data through experimentally validated computational fluid dynamics simulations and building surrogate models using the response surface methodology. This approach offers higher accuracy and broader applicability than empirical models. Moreover, a stepper motor platform based on a speed control scheme is proposed for the first time that can achieve high-precision transient simulation with a simplified control mechanism. This platform was employed to assess the power supply capacity of two hydrokinetic turbines under constant and typical deep-sea flow speeds. The ductless Archimedes screw hydrokinetic turbine was found to be more suitable for the deep water application than the high-solidity horizontal axis turbine due to its high self-start capacity and power coefficient. Furthermore, a case study was conducted on the design of a deep-sea in-situ power generation system at Luzon Undercurrent. Results suggest that a ductless Archimedes screw hydrokinetic turbine with a 1000 mm radius can comfortably cater to the energy demands of miniature AUVs.

FPGA-Based Speed Control Strategy of PMSM Using Improved Beetle Antennae Search Algorithm

To improve performance in terms of overshoot and motor response speed when a permanent-magnet synchronous motor (PMSM) with a proportional–integral (PI) controller is subjected to external disturbances, this paper proposes a speed control strategy based on an enhanced Beetle Antennae Search algorithm, which allows for adjustable parameters of the PI controller within a certain range. Firstly, to enhance the global and local search capabilities of each individual beetle, the step size was improved by linearly decreasing it. Secondly, a simulation model of a PMSM closed-loop control system was built to verify the effectiveness of the improved Beetle Antennae Search (BAS) algorithm. Finally, a linear feedback shift register model that generates four random numbers was developed on a field-programmable gate array (FPGA). The improved BAS algorithm for the PMSM control system was implemented on an FPGA using the Verilog hardware description language, and the feasibility of the system was verified through hardware simulation. Additionally, the hardware resource consumption on different FPGA platforms was analyzed. The simulation results demonstrate that the proposed new speed control strategy can reduce the overshoot and improve the motor response speed.

Ride Comfort Prediction on Urban Road Using Discrete Pavement Roughness Index

The prediction of ride comfort holds significant potential for enhancing the driving experience of both human drivers and autonomous vehicles, as it is closely correlated with pavement roughness. However, in urban road scenarios, the presence of shorter road segments and local irregularities introduces added complexity to ride comfort prediction. To better capture and characterize the irregularities and short road sections’ unevenness, we adopt the discrete roughness index (DRI) instead of the commonly used international roughness index (IRI) for assessing road profile unevenness, which is more suitable for urban roads. Ride comfort prediction is developed through numerical simulations using an eight-degree-of-freedom full-car model. The maximum transient vibration value (MTVV) is adopted to assess ride comfort. Through comparing the correlations between the MTVV and pavement roughness indices, it is indicated that the fitting degree of MTVV-DRI outperforms that of MTVV-IRI on short sections. Then, a set of speed-related DRI thresholds to estimate ride comfort distribution on a given road section is proposed, with considerations of vehicle speed, time period, and wheel paths. A hyperbolic-tangent-based speed control strategy is also proposed to avoid abrupt speed and acceleration changes during deceleration. This prediction method can assist drivers or autonomous vehicles in generating driving control strategies and maintaining a high level of ride comfort.

AGV fuzzy control optimized by genetic algorithms

Abstract Automated Guided Vehicles (AGV) are an essential element of transport in industry 4.0. Although they may seem simple systems in terms of their kinematics, their dynamics is very complex, and it requires robust and efficient controllers for their routes in the workspaces. In this paper, we present the design and implementation of an intelligent controller of a hybrid AGV based on fuzzy logic. In addition, genetic algorithms have been used to optimize the speed control strategy, aiming at improving efficiency and saving energy. The control architecture includes a fuzzy controller for trajectory tracking that has been enhanced with genetic algorithms. The cost function first maximizes the time in the circuit and then minimizes the guiding error. It has been validated on the mathematical model of a commercial hybrid AGV that merges tricycle and differential robot components. This model not only considers the kinematics and dynamics equations of the vehicle but also the impact of friction. The performance of the intelligent control strategy is compared with an optimized PID controller. Four paths were simulated to test the approach validity.

Improved Model Predictive Speed Control of a PMSM via Laguerre Functions

This paper proposes a model predictive speed control strategy for a surface-mounted permanent magnet synchronous motor by applying Laguerre functions. The model predictive controller (MPC) incorporates an integrator. A quadratic programming procedure is applied to solve the constrained optimization problem online. The paper also provides a solution for stability. The performance efficiency of the proposed scheme is validated by comparing the results with the performance of an optimal linear quadratic regulator, conventional state-space model predictive control, and a simple MPC algorithm with integral action. Extensive simulation results confirm the efficacy of the proposed scheme, showing that it achieves good steady-state performance while maintaining a fast dynamic response.

Fuzzy Super-Twisting Sliding Mode Controller for Switched Reluctance Wind Power Generator in Low-Voltage DC Microgrid Applications

There is limited research focused on achieving optimal torque control performance of Switched Reluctance Generators (SRGs). The majority of existing studies tend to favor voltage or power control strategies. However, a significant drawback of SRGs is their susceptibility to high torque ripple. In power generation systems, torque ripple implicates fluctuations in the generated power of the generator. Moreover, high torque ripple can lead to mechanical vibrations and noise in the powertrain, impacting the overall system performance. In this paper, a Torque Sharing Function (TSF) with Indirect Instantaneous Torque Control (IITC) for SRG applied to Wind Energy Conversion Systems (WECS) is proposed to minimize torque ripple. The proposed method adjusts the shared reference torque function between the phases based on instantaneous torque, rather than the existing TSF methods formulated with a mathematical expression. Additionally, this paper introduces an innovative speed control scheme for SRG drive using a Fuzzy Super-Twisting Sliding Mode Command (FSTSMC) method. Notably robust against parameter uncertainties and payload disturbances, the proposed scheme ensures finite-time convergence even in the presence of external disturbances, while effectively reducing chattering. To assess the effectiveness of the proposed methods, comprehensive comparisons are made with traditional control techniques, including Proportional–Integral (PI), Integral Sliding Mode Control (ISMC), and Super-Twisting Sliding Mode Control (STSMC). The simulation results, obtained using MATLAB®/SIMULINK® under various speeds and mechanical torque conditions, demonstrate the superior performance and robustness of the proposed approaches. This study presents a thorough experimental analysis of a 250 W four-phase 8/6 SRG. The generator was connected to a DC resistive load, and the analysis focuses on assessing its performance and operational characteristics across different rotational speeds. The primary objective is to validate and confirm the efficacy of the SRG under varying conditions.

Improved model-free synergetic control of permanent magnet synchronous motor using unknown input observer

Parametric uncertainties and inverter nonlinearity exist in the permanent magnet synchronous motor (PMSM) drive system, which may lead to performance degradation or failure of model-based control. This paper presents an enhanced model-free synergetic control method for PMSMs, addressing the limitations of relying on accurate motor models in existing speed control strategies. The proposed approach aims to achieve robust control performance in the presence of parameter perturbations without the need for an accurate motor model. Firstly, a new ultra-local model of the speed loop for the PMSM is established based on a newly developed ultra-local theory. Subsequently, a model-free synergetic controller is designed using the principles of synergetic control theory. To address the issue of output chattering in the sliding mode observer used to estimate the unknown part of the ultra-local model, a synergetic observer method is proposed. Simulation results are presented and compared with those obtained using a proportional-integral (PI) controller and a conventional model-free sliding mode controller. The results demonstrate that the model-free synergetic controller exhibits robust performance and provides accurate estimation of the unknown part without output chattering.