Electric Power Steering System Research Articles

Genetic algorithm inspired optimal integrated nonlinear control technique for an electric power steering system

Genetic algorithm inspired optimal integrated nonlinear control technique for an electric power steering system

Electromechanical processes in electric power steering system based on permanent magnet synchronous motor

Relevance. Electric power steering of vehicles successfully competes with hydraulic boosters of similar purposes, showing high efficiency, energy saving and environmental friendliness. Aim. To study and research electromechanical processes of electric power steering system based on permanent magnet synchronous motor. Methods. Mathematical and numerical modeling using the Matlab/Simulink software package. Results. The article discusses the operating modes of an electric power steering based on an electric motor with permanent magnets. The authors analyzed two classic schemes for constructing steering systems with electric power steering – with the electric motor placed on the steering column and on the steering rack. Block diagrams of the mechanical and electrical parts of the steering system have been developed. Based on mathematical dependencies characterizing the operating modes of the steering system, a complete dynamic simulation model of the electric power amplifier was obtained on the MATLAB/Simulink platform. Based on a preliminary analysis of control methods and algorithms in the system to solve the problem of control stability, PI controllers with low-pass filters are implemented to regulate the current and voltage of the electric motor depending on the speed of the vehicle and the angle of rotation of the steering wheels. The simulation results were used to design a real electric power steering for a specific vehicle. Conclusions. The complete simulation model of an electric booster based on a synchronous motor with permanent magnets in a car steering system obtained by the authors adequately reflects the dynamic control processes and can be used in the design of automotive vehicles.

Transfer Learning-Based Steering Angle Prediction and Control with Fuzzy Signatures-Enhanced Fuzzy Systems for Autonomous Vehicles

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.

Proposing a novel nonlinear integrated control technique for an electric power steering system to improve automotive dynamic stability

This article proposes a new control solution for an electric power steering (EPS) system to ensure the stability of car's dynamic behaviours. This work provides two new contributions which differ from previously existing publications. Firstly, a novel control method for the steering system is designed in this article based on a combination of proportional-integral-derivative (PID) and backstepping control techniques. The input to the backstepping algorithm is the output of the PID controller, whose parameters are tuned by a complex fuzzy algorithm with two inputs. Secondly, values of road reaction torque and other dynamic effects are calculated using a complex automotive dynamics model based on a nonlinear motion model and a spatial oscillation model. The stability of the control system is evaluated through the Lyapunov control function and the error between the output signals, while the dynamic stability is evaluated through the changes in car's dynamic behaviours. According to the simulation results, output values always closely follow ideal values with negligible errors if and only when the steering system is controlled by the proposed algorithm. In some conditions, the steering motor angle error achieved by the proposed controller does not exceed 0.022 rad, much lower than the fault scenario. In addition, the vehicle's roll angle and motion trajectory always follow the desired value with minimal errors. In conclusion, if the EPS system is controlled by the new control technique shown in this article, car dynamic stability will be guaranteed under all investigated conditions.

Fault Detection Methods for Electric Power Steering System Using Hardware in the Loop Simulation

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.

Diagnosis-based design of electric power steering system considering multiple degradations: role of designable generative adversarial network anomaly detection

Abstract 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.

A novel approach to the FPIBSC strategy for an electric power steering system

The electric power steering system is used to improve comfort and steering feel for the user. In this article, we propose to use an integrated nonlinear control strategy for the electric steering system, which is described based on a complicated dynamic model. This work provides two new contributions. First, this control algorithm combines backstepping and proportional–integral (PI) techniques with parameters adjusted by a fuzzy algorithm, so it is called Fuzzy proportional–integral backstepping control (FPIBSC). The output of the PI algorithm is the input of the backstepping technique, while system stability is evaluated based on the Lyapunov function with virtual control variables. Second, road reaction torque is calculated based on a spatial dynamic model, which considers the influence of many other factors. Numerical simulation methods are used to evaluate the performance of the system. According to research findings, the value of the steering motor angle (controlled object) continuously tracks the desired value with negligible error. Under some specific conditions, the error between signals can be reduced to zero. In addition, other outputs that are obtained from the FPIBSC algorithm also tend to follow the reference signal with high accuracy. The phase difference phenomenon only occurs when using the conventional backstepping algorithm instead of FPIBSC. The assisted torque increases as speed decreases or the driver torque increases. In general, the system’s stability is always guaranteed under many different simulation conditions.

ANALYSIS OF A NEW TYPE OF ELECTRIC POWER STEERINGGEAR WITH TWO PINIONS ENGAGED ON THE SAME SETOF TEETH ON THE RACK

Electric power steering is the most commonly used solution in passenger cars. It is a more economical andreliable solution than the hydraulic equivalent. Its main parts are usually the rack, pinion, and worm wheel,which are subject to wear during driving. A steering gear that works incorrectly poses a direct threat to thesafety of the driver and other road users. The paper presents various types of electric power steering gears,with a particular emphasis on the problems that arise during the design of the pinion and rack. In this paper,a new electric power steering system consisting of a steering gear with two pinions working on a commontoothing of the rack (TPEPS) is proposed. The benefits of placing two pinions on one tooth set of a steeringrack are described, which eliminates many problems during production, and thus allows the reduction inproduction costs. As part of the analysis, design challenges for a new type of transmission are presented,taking into account key customer requirements, with a particular emphasis on durability and ensuring thedesired noise level, which is important due to the replacement of cars with an internal combustion drive withan electric drive. This paper presents selected friction and noise test results for a new power steering system.A comparison is made with a system that has two pinions and different teeth on the rack (DPEPS). The resultsindicate that the new system could become an alternative solution due to its lower friction value and lowernoise.

Zero-Position Offset Calibration of PMSM Based on I/f Control Strategy and Slide Mode Observer for Electric Power Steering System

Zero-Position Offset Calibration of PMSM Based on I/f Control Strategy and Slide Mode Observer for Electric Power Steering System

Multilevel Evaluation Model of Electric Power Steering System Based on Improved Combination Weighting and Cloud Theory

In order to rapidly evaluate the working performance of an Electric Power Steering System (EPS) and clarify the key indicators that affect its working condition, targeted maintenance measures can be taken to improve the vehicle’s handling, stability, and safety. This paper took an EPS system as the research object and decomposed it into five working indicators: steering lightness, steering returnability, steering wheel mid-zone performance, steering wheel vibration, and power assist characteristics. On this basis, the subjective exponential extension Analytic Hierarchy Process (AHP) was combined with the objective improved entropy weight method, and then the combination weighting method of game theory was employed to ensure that the results were reliable. At the same time, a cloud model was introduced to understand the actual situation of each indicator by using cloud parameters and multi-level feedback results. According to the principle of maximum membership and the comprehensive evaluation method, the EPS performance evaluation result was obtained. The results showed that the comprehensive cloud parameter of EPS was U (74.31, 6.08, 0.50), which indicated that its working performance was in good condition, with some minor damage that did not affect its normal use; however, attention should be paid to the maintenance and repair of vulnerable components to ensure the handling, stability, and safety of the vehicle.

Electric power steering system controller design using induction machine

Electric power steering system controller design using induction machine

A Federated Learning Approach for Efficient Anomaly Detection in Electric Power Steering Systems

A Federated Learning Approach for Efficient Anomaly Detection in Electric Power Steering Systems

Lane Keeping Assistance for Electric Power Steering Systems based on the Robust Sliding Mode Observer

Lane Keeping Assistance for Electric Power Steering Systems based on the Robust Sliding Mode Observer

New Road Hazard Classification Enabled by Rack Force Estimation of Electric Power Steering Systems

New Road Hazard Classification Enabled by Rack Force Estimation of Electric Power Steering Systems

Design of Electric Power Steering System Identification and Control for Autonomous Vehicles Based on Artificial Neural Network

Design of Electric Power Steering System Identification and Control for Autonomous Vehicles Based on Artificial Neural Network

Development of a Novel Integrated Control Strategy for Automotive Electric Power Steering Systems

Development of a Novel Integrated Control Strategy for Automotive Electric Power Steering Systems

Designing a new integrated control solution for electric power steering systems based on a combination of nonlinear techniques.

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.

Optimal Design of a Surface Permanent Magnet Machine for Electric Power Steering Systems in Electric Vehicle Applications Using a Gaussian Process-Based Approach

The efficient design optimization of electric machines for electric power steering (EPS) applications poses challenges in meeting demanding performance criteria, including high power density, efficiency, and low vibration. Traditional optimization approaches often fail to find a global solution or suffer from excessive computation time. In response to the limitations of traditional approaches, this paper introduces a novel methodology by incorporating a Gaussian process-based adaptive sampling technique into a surrogate-assisted optimization process using a metaheuristic algorithm. Validation on a 72-slot/8-pole interior permanent magnet (IPM) machine demonstrates the superiority of the proposed approach, showcasing improved exploitation–exploration balance, faster convergence, and enhanced repeatability compared to conventional optimization methods. The proposed design process is then applied to two surface PM (SPM) machine configurations with 9-slot/6-pole and 12-slot/10-pole combinations for EPS applications. The results indicate that the 12-slot/10-pole SPM design surpasses the alternative design in torque density, efficiency, cogging torque, torque ripple, and manufacturability.

Deep Learning-based Motor Prognostics for Electric Power Steering Systems

The electric power steering (EPS) system is a fundamental unit for modern automotive vehicles, providing motor assistance and various control functions to aid the driver's manual maneuvering. Preventing loss of assist (LoA) in advance is critical for EPS systems to mitigate fatal accidents and maintenance costs. As part of the recent interest in intelligent software-defined vehicles (SDV), data-driven approaches have gained much attention to overcome the limitations of conventional fail-safe and maintenance strategies. However, while related works in the field have shown promising results, they are limited to proof-of-concept studies for detecting and isolating artificially injected faults using simulation and test-bed data, which lacks relevance and poses limitations for practical in-vehicle implementation. Herein, we present a deep learning (DL)-based method to predict the motor degradation level in an EPS system using in-vehicle data acquired from the controller area network (CAN) bus. Our approach proposes a novel multivariate transformer (MVT)-based neural twin model of the EPS system, which is trained using an adversarial learning strategy by utilizing only the normal data. Our method can detect degradation levels down to ten percent from normal working conditions based on an anomaly detection mechanism that outperforms baseline methods in quantitative and qualitative measures.

A novel dual power-driven electric power steering system for electric commercial vehicles

To solve the power limitation problem faced by the development of EPS system for commercial vehicles and achieve the high-performance steering, this article presents a novel dual power-driven electric power steering system (DPEPS) for commercial vehicles by using the open winding permanent magnet synchronous motor (OPMSM) fed by dual inverter with high and low DC voltage supplies. First, the two-level control strategy is introduced for the developed DPEPS system, where the upper control is designed to make the actual steering follow the expected steering performance as determined by the assistant torque curve and to generate the appropriate target current according to the vehicle speed and steering torque. And then the lower control consists of the synthetical space vector control scheme and dual PI control loop is used to execute commands from the upper control, which can make the motor execute the appropriate power supply method according to the steering torque and assist current. Finally, a co-simulation model is carried out for an electric bus, and based on which the proposed DPEPS system is validated.