Integrated strategy for vehicle dynamics stability considering the uncertainties of side‐slip angle

Dong, Xiaoxu; Zhang, Lipeng; Cheng, Shuo; Wang, Zhanchao

doi:10.1049/iet-its.2019.0461

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…Before any control algorithm is put into practical applications, it must be modeled theoretically to gain a deep understanding and validated by numerical simulations to reduce the high cost of practical testing [8]. The low-DOF model is used to capture the characteristics of the idealized control target of the vehicle, including other secondary parameters, such as slip angle [9][10][11][12][13], yaw angle [9,13,14], and yaw torque [15,16]. Gang et al [10] developed a 4 DOF model to describe the steering stability of the vehicle by calculating the slip angle, thus verifying the effectiveness of the differential algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…Liang et al [15] also established a 2 DOF model to understand the driving dynamics and steering stability of the vehicle by calculating the yaw moment and verified the advantages of the genetic algorithm over the traditional PID algorithm. The high DOF models are used to realistically simulate a wide range of vehicle performance in real-world driving, such as handling performance [17][18][19] and dynamic performance [13,[20][21][22]. Ni et al [17] developed an 11 DOF vehicle dynamics model to simulate the handling performance of a hybrid eight-wheel drive vehicle by modeling and correlating each part of the complex vehicle system.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Modeling, Simulation, and Optimization of Vehicle Electronic Stability Program Algorithm Based on Back Propagation Neural Network and PID Algorithm

Wu,

Kang,

et al. 2024

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The vehicle lateral stability control algorithm is an essential component of the electronic stability program (ESP), and its control effect directly affects the vehicle’s driving safety. However, there are still numerous shortcomings and challenges that need to be addressed, including enhancing the efficiency of processing intricate pavement condition data, improving the accuracy of parameter adjustment, and identifying subtle and elusive patterns amidst noisy and ambiguous data. The introduction of machine learning algorithms can address the aforementioned issues, making it imperative to apply machine learning to the research of lateral stability control algorithms. This paper presented a vehicle lateral electronic stability control algorithm based on the back propagation (BP) neural network and PID control algorithm. Firstly, the dynamics of the whole vehicle have been analytically modeled. Then, a 2 DOF prediction model and a 14 DOF simulation model were built in MATLAB Simulink to simulate the data of the electronic control units (ECU) in ESP and estimate the dynamic performance of the real vehicle. In addition, the self-correction of the PID algorithm was verified by a Simulink/CarSim combined simulation. The improvement of the BP neural network to the traditional PID algorithm was also analyzed in Simulink. These simulation results show the self-correction of the PID algorithm on the lateral stability control of the vehicle under different road conditions and at different vehicle speeds. The BP neural network smoothed the vehicle trajectory controlled by traditional PID and improved the self-correction ability of the control system by iterative training. Furthermore, it shows that the algorithm can automatically tune the control parameters and optimize the control process of the lateral electronic stability control algorithm, thus improving vehicle stability and adapting it to many different vehicle models and road conditions. Therefore, the algorithm has a high practical value and provides a feasible idea for developing a more intelligent and general vehicle lateral electronic stability system.

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