A new controller design method for an electric power steering system based on a target steering torque feedback controller

Aiming at the high demand for the torque response speed and torque pulsation of the all-terrain vehicle (ATV) Electric Power Steering (EPS) system, this paper proposes to apply the Model Predictive Current Control to the all-terrain vehicle EPS system. A Novel Three-Vector Model Predictive Current Control (N3V-MPCC) is proposed in this paper to reduce the current ripple and reduce the calculation load. Two effective voltage vectors and a zero vector are selected in the control period through only six times of prediction and application of the Sector Vector Selection method. The duration of each voltage is calculated and output to the Voltage Source Inverter (VSI). Simulation and experimental results show that, compared with PID cascade Field Oriented Control (PI-FOC), N3C-MPCC can effectively reduce the ripple current of the d-axis and the q-axis. In the simulated electric power mode, the q-axis current ripple of the N3V-MPCC is reduced by 66.67%. Experimental results show that the current ripple of the motor is reduced by 60%, and the torque pulsation is reduced by 62.5%. Therefore, N3V-MPCC has a faster current response speed and smooth steering torque.

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Research on Control Strategy of the Electric Power Steering System for All-Terrain Vehicles Based on Model Predictive Current Control

Electric power-assist steering (EPAS) system is an auxiliary system of electric vehicle (EV) that reduces the steering effort of the driver by applying an assist toque using an electric motor to the steering system. This paper aims to present a comparative analysis of the salp swarm algorithm (SSA) tuned 2-Degree of freedom (DOF) PID controller and the PID controller with a derivative filter (PIDF) in reducing the current consumption of the EPAS system besides in lowering the driving effort. Simulation results are discussed to effectively signify the comparison between the two controllers. This study also determines the feasibility of SSA in optimally tuning the controller parameters for the EPAS system.

Comparative Analysis of Salp Swarm Algorithm Tuned 2-DOF PID and PIDF controllers for Reducing the Current Consumption in EPAS System of Electric Vehicles

As anomaly detection for electrical power steering (EPS) systems has been centralized using model- and knowledge-based approaches, EPS system have become complex and more sophisticated, thereby requiring enhanced reliability and safety. Since most current detection methods rely on prior knowledge, it is difficult to identify new or previously unknown anomalies. In this paper, we propose a deep learning approach that consists of a two-stage process using an autoencoder and long short-term memory (LSTM) to detect anomalies in EPS sensor data. First, we train our model on EPS data by employing an autoencoder to extract features and compress them into a latent representation. The compressed features are fed into the LSTM network to capture any correlated dependencies between features, which are then reconstructed as output. An anomaly score is used to detect anomalies based on the reconstruction loss of the output. The effectiveness of our proposed approach is demonstrated by collecting sample data from an experiment using an EPS test jig. The comparison results indicate that our proposed model performs better in detecting anomalies, with an accuracy of 0.99 and a higher area under the receiver operating characteristic curve than other methods providing a valuable tool for anomaly detection in EPS.

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A Deep Learning Approach to Detect Anomalies in an Electric Power Steering System

This study presents a series of analyzes considering the traction and steering demands of an autonomous electric vehicle (AEV) as a shuttle. The considered analyzes in here are dealt with as driving cycle (DC) and driving scenarios (DS) to assess the traction and steering performance of the AEV. The aim of this study is to evaluate the issues such as over engineering for AEV traction and steering motor requirements on a certain route by comparatively analyzing traditional and dynamic calculation under the DC and DS. Therefore, DC and DS in the lit-erature are evaluated in terms of different applications, optimization techniques, generation algorithm, parametric characterization, e-motor type etc. Afterwards, NEDC, US06, WLTC, Double Lane Change (DLC), Constant Radius (CR) and Slowly Increase Steer (SIS) are determined. Then, they are arranged according to the vehicle-specific limits on an electric golf car. The modified DCs and DSs are run on the dynamic model of the vehicle. In the performed analysis, the parame-ters such as reference trajectory tracking, yaw angle, tractive and steering forces, lateral and longitudinal displacement-acceleration, steering and traction motor power–speed-torque are investigated. And the obtained results are evaluated by comparing the traditional calculation results.

Investigation on Different Driving Cycle and Scenarios Considering the Autonomous Electric Vehicles

In the electric power steering (EPS) system, low-frequency disturbances such as road resistance, irregular mechanical friction, and changing motor parameters can cause steering wheel torque fluctuation and discontinuity. In order to improve the steering wheel torque smoothness, an improved torque control method of an EPS motor is proposed in the paper. A target torque algorithm is established, which is related to steering process parameters such as steering wheel angle and angular speed. Then, a target torque closed-loop control strategy based on the improved ADRC is designed to estimate and compensate the internal and external disturbance of the system, so as to reduce the impact of the disturbance on the steering torque. The simulation results show that the responsiveness and anti-interference ability of the improved ADRC is better than that of the conventional ADRC and PI. The vehicle experiment shows that the proposed control method has good motor current stability, steering torque smoothness, and flexibility when there is low-frequency disturbance.

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Chao Zhang

Papers

Torque Control of Electric Power Steering Systems Based on Improved Active Disturbance Rejection Control

Design and research of a novel non-contact vertical inductive torque sensor