Search

The question of how education research can be 'useful' is an enduring and challenging one. In recent years, this question has been approached by universities through a widespread 'impact' agenda. In this article, we explore the tensions between usefulness and impact and present six stories that reflect on research use with communities. These stories engage issues of the risk of usefulness, the time that is needed to work collaboratively for research usefulness, whether theories developed in universities can be useful to communities for understanding the problems they face, who has the power to steer research to serve their purposes, and how community collective action can enhance the usefulness of research. The article concludes with a section that reflects on the importance of continuing to engage with the debates about research use in often highly commercially oriented university environments. This article brings together diverse voices that wrestle with the politics of research use beyond the neat, linear narratives of change that impact agendas tend to portray. These illustrations of the ethical dilemmas encountered through navigating research use with communities contribute to an ongoing conversation about refusing capitalist and colonialist logics of research extraction while working within institutions often driven by such logics. [ABSTRACT FROM AUTHOR]

An EPS system is used to improve the stability and safety of the car when steering while also simplifying the steering process. This article introduces a novel control solution for the EPS system called BSSMCPID. This algorithm combines two nonlinear techniques, BS and SMC, with the input signal corrected by a PID technique. This algorithm provides three new contributions compared to existing algorithms: reducing system errors and eliminating phase differences, ensuring stability even when exposed to external disturbances, and reducing power consumption. The system's stability is evaluated according to the Lyapunov criterion, while the algorithm's performance is evaluated based on numerical simulation results. According to the article findings, the RMS error of the steering column angle and steering motor angle values (controlled objects) is approximately zero, and the RMS error of the steering column speed and steering motor speed is about 0.01 rad/s, which is much lower than the results obtained with traditional BS and PID controllers. When the EPS system is controlled by the integrated nonlinear method proposed in this work, the output values always closely follow the reference values with negligible errors under all investigated conditions. Additionally, power steering performance increases as speed decreases or driver torque increases, which follows the ideal assisted power steering curve. In general, the responsiveness and stability of the system are always ensured when applying this algorithm. [ABSTRACT FROM AUTHOR]

This research introduces an innovative approach for End-to-End steering angle prediction and its control in electric power steering (EPS) systems. The methodology integrates transfer learning-based computer vision techniques for prediction and control with fuzzy signatures-enhanced fuzzy systems. Fuzzy signatures are unique multidimensional data structures that represent data symbolically. This enhancement enables the fuzzy systems to effectively manage the inherent imprecision and uncertainty in various driving scenarios. The ultimate goal of this work is to assess the efficiency and performance of this combined approach by highlighting the pivotal role of steering angle prediction and control in the field of autonomous driving systems. Specifically, within EPS systems, the control of the motor directly influences the vehicle's path and maneuverability. A significant breakthrough of this study is the successful application of transfer learning-based computer vision techniques to extract respective visual data without the need for large datasets. This represents an advancement in reducing the extensive data collection and computational load typically required. The findings of this research reveal the potential of this approach within EPS systems, with an MSE score of 0.0386 against 0.0476, by outperforming the existing NVIDIA model. This result provides a 22.63% better Mean Squared Error (MSE) score than NVIDIA's model. The proposed model also showed better performance compared with all other three references found in the literature. Furthermore, we identify potential areas for refinement, such as decreasing model loss and simplifying the complex decision model of fuzzy systems, which can represent the symmetry and asymmetry of human decision-making systems. This study, therefore, contributes significantly to the ongoing evolution of autonomous driving systems. [ABSTRACT FROM AUTHOR]

Copyright of Automotive Engineer (1674-6546) is the property of Auto 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.)

To solve the complicated steering control of small vehicles in the agriculture and difficult steering on complex roads, this study designed a wheel-track composite vehicle. The vehicle incorporated a novel power differential steering mechanism with dual driving, enabling steering through the differential rotation of the rear two wheels. The vehicle is simple to control, small in size, and is able to work under the conditions of complex roads, such as hills, mountains, and muddy land. The study initially focused on presenting the design, theoretical analysis, and dynamic simulation analysis of the power differential steering mechanism with dual driving. Subsequently, the vehicle underwent modeling and simulation using UG software to validate the reasonability of the values. Finally, utilizing test data, four mathematical models for the actual steering radius of the vehicle on four road surfaces were derived through neural network fitting. The maximum relative error between the model results and the actual steering radius value was reported to be 3.53%. The advantages of the vehicle included continuous radius steering, deceleration and torsion increase, differential lock, etc. This made it well-suited for applications in all-terrain military and civilian vehicles, as well as various special equipment mobile platforms equipped with walking devices. [ABSTRACT FROM AUTHOR]

Recently, interest in functional safety has surged because vehicle technology increasingly relies on electronics and automation. Failure of certain system components can endanger driver safety and is costly to address. The detection of abnormal data is crucial for enhancing the reliability, safety, and efficiency. This study introduces a novel anomaly-detection method of designable generative adversarial network anomaly detection (DGANomaly). DGANomaly combines the data augmentation method of a designable generative adversarial network (DGAN) with a generative adversarial network anomaly-detection data classification technique. DGANomaly not only generates virtual data that are challenging to obtain or simulate but also produces a range of statistical design variables for normal and abnormal data. This approach enables the specific identification of normal and abnormal design variables. To demonstrate its effectiveness, the DGANomaly method was applied to an electric power steering (EPS) model when multiple degradations of gear stiffness, gear friction, and rack displacement were considered. An EPS model was constructed and validated using simulation programs such as Prescan, Amesim, and Simulink. Consequently, DGANomaly exhibited a higher classification accuracy than the other methods, allowing for more accurate detection of abnormal data. Additionally, a clearer range of statistical designs can be obtained for normal data. These results indicate that the statistical design variables that are less likely to fail can be obtained using minimal data. [ABSTRACT FROM AUTHOR]

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

Excessive cogging torque can cause torque fluctuations, noise, and vibration in electric power steering (EPS) motors, which is a key factor in the high-precision and high-performance optimization design of EPS motors for electric vehicles. This article takes a 12-slot 10-pole electric power steering motor for a certain car as an example. By establishing the corresponding electromagnetic field model and theoretical analysis of the motor, the influence of the pole arc coefficient and eccentricity parameters of the permanent magnet on the cogging torque of the electric power steering motor is explored. A comprehensive optimization scheme for reducing the cogging torque of the motor structure is proposed. The effectiveness of the designed scheme was verified through finite-element simulation and experimental testing of motor electromagnetism. Compared with the original design, the optimized structure of the EPS motor resulted in an 86.62% reduction in cogging torque during experimental testing. [ABSTRACT FROM AUTHOR]

The development of the automotive industry is associated with the rapid advancement of onboard systems. In addition, intensive development in the electronics and control systems industry has resulted in a change in the approach to the issue of assistance systems in vehicles. Classic hydraulic systems have been almost completely replaced by modern electric power steering (EPS) systems, especially in citizen vehicles. This paper focuses on fault detection algorithms for EPS, along with the available tools to aid development and verification. The article discusses in detail the current state of knowledge in this area. The principle of operation of the EPS system and the influence of the structure of the mechanical system on its operation, in particular the characteristics of the ground–tire contact, are presented. Various error identification methods are presented, including those based mainly on a combination of tests of real objects as well as those combined with modern hardware-in-the-loop (HIL) equipment and virtual vehicle environment software, enabling the development of new diagnostic methods, enhancing the security, reliability, and energy control in the vehicle. A review of the literature indicates that although many algorithms which enable fault detection at an early stage are described, their potential for use in a vehicle is highly limited. The reason lies in simplifications, including models and the operating EPS temperature range. The most frequently used simplification of the model is its linearization, which significantly reduces the calculation time; however, this significantly reduces the accuracy of the model, especially in cases with a large range of system operation. The need for methods to detect incipient faults is important for the safety and reliability of the entire car, not only during regular use but also especially during life-saving evasive maneuvers. [ABSTRACT FROM AUTHOR]

Aiming at the lack of adaptability of vehicle parameters under extreme conditions, this paper proposes an integrated control method for path tracking and lateral stability of distributed drive electric vehicles based on tire cornering stiffness adaptive model predictive control (MPC) scheme. The control method integrates active front steering and direct yaw control to improve the path tracking and lateral stability performance of distributed drive electric vehicles. Firstly, considering the influence of vertical load transfer, the tire cornering stiffness is estimated based on the extended Kalman filter (EKF) algorithm. Then, using this online updated tire cornering stiffness value, an adaptive MPC controller for path tracking and lateral motion stability of distributed drive electric vehicles is constructed. Meanwhile, a fuzzy sliding mode control (Fuzzy-SMC)–based longitudinal velocity controller is established to ensure the accuracy of velocity tracking. Also, according to the distributed driving characteristics of the controlled system, a tire torque distributor based on weighted pseudo-inverse (WPI) is designed with the minimum tire load rate as the optimization objective, where the road adhesion condition and the maximum output torque of the motor are considered as constraints. The simulation results show that the proposed integrated control method based on tire cornering stiffness adaptive model predictive control is robust and effective. Compared with constant cornering stiffness model predictive control–based control method, it can improve the vehicle path tracking accuracy and lateral motion stability under extreme conditions. [ABSTRACT FROM AUTHOR]

The steer-by-wire (SbW) system is a promising system in the realm of automotive engineering. It substitutes the mechanical connection between the steering wheel and the front road wheels with an electronic signal-based functional connection. The SbW system offers several advantages over conventional steering systems, including weight reduction, reduced vibration, and enhanced steering functionality configuration. However, the absence of a mechanical linkage in the SbW system gives rise to certain challenges. The SbW system requires endowing adequate steering feel such as damping and reaction force using feedback motor, and the road wheel needs robust control of pinion motor for normal load variation by passengers and self-aligning torque as external disturbance. The SbW system is composed of the steering feedback module (SFM) and the road wheel module (RWM). This paper proposes a control approach to generate steering feel for SFM, in which steering feel is generated using an admittance model based on velocity control. A disturbance observer is applied to ensure robustness of velocity control. The steering wheel torque versus steering wheel angle (T–A) curve is used to analyze steering feel characteristic and evaluate steering feel. The proposed steering system is validated through experiments that confirm its ability to provide satisfactory steering feel for vehicles. This work may offer a novel solution for the design of advanced steering systems in the field for the future mobility such as an autonomous driving. [ABSTRACT FROM AUTHOR]

To address the conflict of inconsistent driving intentions between the driver and driving assistance system in cooperative steering, a human–machine cooperative steering control framework based on non-cooperative Nash game is proposed. The competitive relationship between the driver and driving assistance system with different control objectives is modeled as a non-cooperative Nash game. The cooperative steering under human–machine Nash game condition is transformed into a model predictive control optimization problem to achieve coordination through Nash equilibrium. A flexible driving authority allocation strategy based on time-to-collision is then proposed for vehicle lateral control, with dynamic driving authority weights fed into the human–machine cooperative steering model in real time. The effectiveness of the non-cooperative Nash game human–machine cooperative steering strategy and flexible driving authority allocation strategy is validated through simulations and hardware-in-the-loop experiments. Results show the proposed strategy can improve vehicle safety while retaining certain driver freedom when human–machine control targets conflict, by flexibly transferring authority from the driver to the assistance system as collision risk increases. [ABSTRACT FROM AUTHOR]

A hub motor is integrated into an electric wheel. The external excitation is complex and the heat dissipation conditions are poor. The working temperature of the hub motor easily becomes too high, resulting in large fluctuations in the output torque, which affect its service life. Taking a four-wheel hub-driven electric vehicle as the research object and aiming to resolve the issue of inaccurate prediction of the output torque of the hub motor in the real operating environment of the vehicle, a method for analyzing the temperature rise and torque characteristics of the hub motor considering multisource excitation and magnetic–thermal bidirectional coupling is proposed. First, the multisource excitation transmission path of the hub motor and the coupling principle of the road-electric wheel-vehicle body suspension system are analyzed from three aspects: the electromagnetic effect of the hub motor itself, the tire-ground effect, and the interaction between suspension (body) and electric wheel. We concluded that the load torque and air gap change in the motor are the key factors of its torque characteristics. On this basis, a dynamic model of the road-electric wheel-suspension-vehicle body system, an electromagnetic field model of the hub motor, and a temperature field model are established, and the influence of load torque and air gap change on the loss of in-wheel motor under multisource excitation is analyzed. Furthermore, based on the magnetic–thermal bidirectional coupling method, the motor loss under the combined action of load torque and air gap change is introduced into the temperature field model, and combined with the electromagnetic field model of the hub motor, the temperature distribution law and torque characteristics of the hub motor are accurately predicted. Finally, the accuracy and effectiveness of the calculation results of the temperature and torque characteristics of the hub motor are verified via an electric wheel bench test. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue on vehicle dynamics and road safety. The articles included in the issue cover a wide range of topics related to engineering-based technologies and measures for improving road safety and ride comfort. The articles discuss various aspects such as the impact of pavement aggregate grain size on skid resistance, technical solutions for bridge refurbishment, the effect of steering inputs on vehicle response to crosswinds, and the characterization of forces in the transportation of bulk and liquid commodities. The special issue provides readers with insights into the progress and achievements of different countries in the field of vehicle dynamics and road safety, and highlights advanced solutions for autonomous driving research, climate change aspects in transport, vehicle functional safety, and driver assistance systems. [Extracted from the article]

Power steering technology help human to control the car. The hydraulic power steering system now tends to be replaced by the electric power steering system (EPS). As the main driver that require precise control. The contribution of this research is to obtain system identification of EPS motor and novelty control strategy to achieve stable control better. Motor control require an appropriate mathematical model and updown-up down signals of Pseudo Random Binary Signal Sequence (PRBS) were used. The modelling method used was the Numerical Algorithm for Subspace State Space System Identification (N4SID). The quality of the modeling needs to be measured to see whether it was close to the original signal. The validation of the model obtained tested using Variance Accounted For (VAC), Akaike Information Criterion (AIC), and Final Prediction Error (FPE). The best mathematical model was developed on the basis of these three criteria, which is 3rd order model. The control strategy carried out by means of the Ziegler Nichols, Tyreus Luyben and Haugen tuning technique. With these three tuning methods, the control parameters obtained were used for Proprotional-Integral (PI) and Proportional-Integral-Derivative (PID) control. Based on the study, the Haugen control shows the best results of the two other controls, namely with a rise time value of 11,361 ms, overshoot of 6,898%, and steady state at 1.3 s. This show that PI control using the Haugen tuning method able to control the motor well. Robustness tests have also been carried out because the steering system is operated in unpredictable environmental conditions. The control greatly influenced the performance and stability of EPS control in the car's steering system. [ABSTRACT FROM AUTHOR]

This paper presents the design and the non-linearity optimization of a new vertical non-contact angle sensor based on the electromagnetic induction principle. The proposed sensor consists of a stator part (with one solenoidal excitation coil and three sinusoidal receiver coils) and a rotor part (with six rectangular metal sheets). The receiver coil was designed based on the differential principle, which eliminates the effect of the excitation coil on the induced voltage of the receiver coil, and essentially decouples the excitation field from the eddy current field. Moreover, the induced voltages in the three receiver coils are three-phase sinusoidal signals with a phase difference of 10°, which are linearized by CLARK transformation. To minimize the sensor non-linearity, the Plackett–Burman technique was used, which identified the stator radius and the rotor blade thickness as the key factors affecting the sensor linearity. Then, the particle swarm algorithm with decreasing inertia weights was utilized to optimize the sensor linearity. A sensor prototype was made and tested in the laboratory, where the experimental results showed that the sensor non-linearity was only 0.648% and 0.645% in the clockwise and counterclockwise directions, respectively. Notably, the non-linearity of the sensor was less than −0.696% at different speeds. [ABSTRACT FROM AUTHOR]

In this paper, a novel electric-hydraulic power steering (EHPS) system and a vehicle stability coordination control algorithm are proposed which can not only ensure the accuracy of the trajectory tracking but also solve the coordination control problem between the stability of the lateral control and the stability of the roll in the extreme condition. Firstly, the EHPS system is designed to provide accurate control input of front wheel angle for vehicle lateral dynamics control. Secondly, on the basis of optimal preview theory, a new trajectory tracking fusion controller combined with sliding mode control is proposed to improve the accuracy and stability of the system in the process of vehicle lateral trajectory tracking control. Then, the stability domain boundary function of the phase plane is determined according to the phase plane of the sideslip angle-yaw rate, and the stability margin of the phase plane is calculated during the steering process. Finally, considering the tracking accuracy, lateral stability and roll stability performance in the process of trajectory tracking, the linear weighted algorithm is used to coordinate above three objectives, and the HIL bench test and real vehicle experiment verify that the proposed algorithm has good reliability and effectiveness. [ABSTRACT FROM AUTHOR]

An advanced driver-assistance system (ADAS) is critical to driver–vehicle-interaction systems. Driving behaviour modelling and control significantly improves the global performance of ADASs. A haptic assistive system assists the driver by providing a specific torque on the steering wheel according to the driving–vehicle–road profile to improve the steering control. However, the main problem is designing a compensator dealing with the high-level uncertainties in different driving scenarios with haptic driver assistance, where different personalities and diverse perceptions of drivers are considered. These differences can lead to poor driving performance if not properly accounted for. This paper focuses on designing a data-driven model-free compensator considering various driving behaviours with a haptic feedback system. A backpropagation neural network (BPNN) models driving behaviour based on real driving data (speed, acceleration, vehicle orientation, and current steering angle). Then, the genetic algorithm (GA) optimises the integral time absolute error (ITEA) function to produce the best multiple PID compensation parameters for various driving behaviours (such as speeding/braking, lane-keeping and turning), which are then utilised by the fuzzy logic to provide different driving commands. An experiment was conducted with five participants in a driving simulator. During the second experiment, seven participants drove in the simulator to evaluate the robustness of the proposed combined GA proportional-integral-derivative (PID) offline, and the fuzzy-PID controller applied online. The third experiment was conducted to validate the proposed data-driven controller. The experiment and simulation results evaluated the ITAE of the lateral displacement and yaw angle during various driving behaviours. The results validated the proposed method by significantly enhancing the driving performance. [ABSTRACT FROM AUTHOR]

Data acquisition from a vehicle operating in real driving conditions is extremely useful for analyzing the real-time behavior of the vehicle and its components. A few studies have measured the real-time data for a four-wheeler electric vehicle. However, no attempts have been reported to measure the real-time data and find the inverter efficiency for a two-wheeler electric vehicle. The present work has accomplished successful real-time data acquisition from a two-wheeler electric vehicle. The real-time current and voltage coming in and going out from the inverter, frequency of the motor operation, power factor, distance covered, and velocity have been measured. The inverter efficiency is found to be over 95% for over 80% of the total drive time, and the power factor for the motor is over 0.8 for almost 50% of the total drive time. A few insights on driver behavior and finally the torque-speed characteristics and two quadrant operation of the motor are discussed. [ABSTRACT FROM AUTHOR]

Automatic guidance of agricultural vehicles requires automatic execution of operation commands received from the navigation controller by using electronically controlled mechanisms for wheel steering, speed changing and work implementing. Automatic steering contributes as a prerequisite technique in automatic and semi-automatic agricultural navigation. This research aimed to develop an electric automatic steering system that was compact in its structure and integrated into original steering mechanism in a simply and convenient way for aftermarket modification. A brushless motor and reducer assembly was utilized to provide an adequate steering torque instead of manual maneuver. A rapid assembling approach was proposed by passing the steering shaft through the hollow output shaft. A digital proportional-integral-differential (PID) algorithm was implemented to calculate the rotation speeds and directions by comparing the desired angle and the actual angle, which was implemented in a printed circuit board with a microcontroller unit (MCU) and interface chips. An unmanned wheeled tractor was applied as test platform to integrate the newly developed electric automatic steering system. Tests were conducted to evaluate its performance in terms of stability and responsiveness. An autonomous navigation system guided the tractor along target paths in the field by sending steering commands to the electric automatic steering system. The results show that the steering angle error was less than 0.81° when desired steering angle was less than 10°. The lateral error difference was no more than 4.76 cm when repeating following the same target path, which indicated that the electric automatic steering system responded accurately and robustly to steering commands. [ABSTRACT FROM AUTHOR]

Copyright of Automotive Engineer (1674-6546) is the property of Auto 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.)

Since electric power steering has replaced hydraulic power steering in passenger cars, steering feedback has become a challenging task in steering system development. Test benches represent a valid approach for improving steering feedback since they allow investigations without the real vehicle. To improve the applicability of feedback evaluations on a steering test bench, this paper aims to identify improvements in the current evaluation technique to obtain objective parameters that correlate with a subjective evaluation of safety-relevant steering feedback. Therefore, a previously reported approach of a chirp rack force excitation, using the magnitude of the transfer function from the rack force to steering wheel torque to describe steering feedback, is compared to a similar identification approach in which a pseudo-random-binary-sequence signal is utilized. To reflect realistic applications, driving maneuvers are transferred to the test bench to identify relevant objective data. For a valid representation of the steering wheel operation, a human grip model is implemented and compared to a fixed steering wheel angle control. It is shown that the random signal represents a valid and, on average, improved approach to objectively evaluating the steering feedback. Furthermore, a recommendation can be made to include the human grip model in the feedback evaluation tests, as the identified correlation results are improved by its inclusion. The identified parameters and methods represent an improvement for future steering feedback development. [ABSTRACT FROM AUTHOR]