Composite Active Front Steering Controller Design for Vehicle System

Diao, Xiaoxu; Yang, Jian; Ma, Li; Ding, Shihong; Jiang, Haobin

doi:10.1109/access.2017.2693687

Cited by 21 publications

(5 citation statements)

References 25 publications

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“…The side wind force used in this study is shown in Fig. 13 (a), which consists of a ramp, step, and sinusoidal signals [48]. It can be seen from Fig.…”

Section: G Robustness Analysismentioning

confidence: 99%

Brake Fault Identification and Fault-Tolerant Directional Stability Control of Heavy Road Vehicles

2020

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Accurate fault diagnosis in air brake is crucial to reduce frequent brake inspection and maintenance in heavy commercial road vehicles. Existing model-based fault diagnostic schemes work well under limited vehicle operating conditions, which is insufficient for developing an on-board monitoring device. In this context, a learning-based fault identification scheme using the Random Forest technique, which accommodates the vehicle's wide operating conditions, is proposed. This scheme identifies the brake's fault levels with a better classification accuracy of 92% compared to techniques such as Naïve Bayes, k-Nearest Neighbors, Support Vector Machine, and Decision Tree. Further, a fault-tolerant controller is proposed to overcome the vehicle's directional instability arising due to the brake fault. Two sliding mode controllers, namely differential brake control and steering angle control, were developed to control the yaw angle. These have been implemented in a Hardware in Loop experimental platform with the vehicle dynamic simulation software TruckMaker ® .

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“…The side wind force used in this study is shown in Fig. 13 (a), which consists of a ramp, step, and sinusoidal signals [48]. It can be seen from Fig.…”

Section: G Robustness Analysismentioning

confidence: 99%

Brake Fault Identification and Fault-Tolerant Directional Stability Control of Heavy Road Vehicles

2020

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“…These are the non-uniform road adhesion and side wind forces. The effect of side wind force on the vehicle depends on vehicle body contour, steering geometry, and vehicle mass, according to Diao et al [2]. The latter two factors are usually constant for the vehicle, whereas the body contour can be amended to improve stability.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, the algorithm provides good control performance in a range of conditions using a simplified vehicle model. Similarly, Diao et al [2] focused on the development of an active front wheel steering system using sideslip angle and sliding mode observer design to reject the unwanted side wind. The algorithm is verified using two degree of freedom integrate with a vehicle dynamic simulation tool called CarSim software.…”

Section: Introductionmentioning

confidence: 99%

Active Front Wheel Steering System using Yaw Rate Estimation based Fuzzy Logic Due to Various Lateral Wind Disturbance

Aparow

Lun

2022

Int. J. Automot. Mech. Eng.

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The paper devised and compared the performances of PID, fuzzy-tuned PID and fuzzy logic controller in an Active Front Wheel Steering system to stabilize a 9-DOF nonlinear passenger vehicle when subjected to lateral wind disturbance. The vehicle model was derived mathematically and verified with data from IPG CarMaker at a longitudinal speed of 80 km/h. Initially, the disturbance test was conducted using three lateral wind disturbance profiles to test for controller resiliency with zero steering input. Then, a simple but effective yaw rate observer was derived without compromising the linearity of the vehicle model to simulate the disturbance test with a double lane change (DLC) steering input. A more extreme disturbance magnitude was evaluated in the latter test using the developed control designs. The three controllers showed good performances in both disturbance tests, with fuzzy logic having the lowest error out of the three, which is less than 5% for using the estimated yaw rate observer.

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“…By analyzing a large number of drivers' long driving mode, road conditions, weather conditions, and so on, the influence of the electric vehicle at different driving speed, acceleration, and temperature on the energy consumption rate is calculated. However, in the estimation of electric vehicle mileage, it does not consider the influence of motor control strategy [16,17]. Therefore, it is necessary to study the regenerative braking strategy of a dual-axis drive electric vehicle [18].…”

Section: Introductionmentioning

confidence: 99%

Energy Recovery Strategy Numerical Simulation for Dual Axle Drive Pure Electric Vehicle Based on Motor Loss Model and Big Data Calculation

2018

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Aiming at the braking energy feedback control in the optimal energy recovery of the two-motor dual-axis drive electric vehicle (EV), the efficiency numerical simulation model based on the permanent magnet synchronous motor loss was established. At the same time, under different speed and braking conditions, based on maximum recovery efficiency and data calculation of motor system, the optimization motor braking torque distribution model was established. Thus, the distribution rule of the power optimization for the front and rear electric mechanism was obtained. This paper takes the Economic Commission of Europe (ECE) braking safety regulation as the constraint condition, and finally, a new regenerative braking torque distribution strategy numerical simulation was developed. The simulation model of Simulink and CarSim was established based on the simulation object. The numerical simulation results show that under the proposed strategy, the average utilization efficiency of the motor system is increased by 3.24% compared with the I based braking force distribution strategy. Moreover, it is 9.95% higher than the maximum braking energy recovery strategy of the front axle. Finally, through the driving behavior of the driver obtained from the big data platform, we analyze how the automobile braking force matches with the driver’s driving behavior. It also analyzes how the automobile braking force matches the energy recovery efficiency. The research results in this paper provide a reference for the future calculation of braking force feedback control system based on big data of new energy vehicles. It also provides a reference for the modeling of brake feedback control system.

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Composite Active Front Steering Controller Design for Vehicle System

Cited by 21 publications

References 25 publications

Brake Fault Identification and Fault-Tolerant Directional Stability Control of Heavy Road Vehicles

Brake Fault Identification and Fault-Tolerant Directional Stability Control of Heavy Road Vehicles

Active Front Wheel Steering System using Yaw Rate Estimation based Fuzzy Logic Due to Various Lateral Wind Disturbance

Energy Recovery Strategy Numerical Simulation for Dual Axle Drive Pure Electric Vehicle Based on Motor Loss Model and Big Data Calculation

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