scholarly journals Extension Coordinated Multi-Objective Adaptive Cruise Control Integrated with Direct Yaw Moment Control

Actuators ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 295
Author(s):  
Hongbo Wang ◽  
Youding Sun ◽  
Zhengang Gao ◽  
Li Chen

An adaptive cruise control (ACC) system can reduce driver workload and improve safety by taking over the longitudinal control of vehicles. Nowadays, with the development of range sensors and V2X technology, the ACC system has been applied to curved conditions. Therefore, in the curving car-following process, it is necessary to simultaneously consider the car-following performance, longitudinal ride comfort, fuel economy and lateral stability of ACC vehicle. The direct yaw moment control (DYC) system can effectively improve the vehicle lateral stability by applying different longitudinal forces to different wheels. However, the various control objectives above will conflict with each other in some cases. To improve the overall performance of ACC vehicle and realize the coordination between these control objectives, the extension control is introduced to design the real-time weight matrix under a multi-objective model predictive control (MPC) framework. The driver-in-the-loop (DIL) tests on a driving simulator are conducted and the results show that the proposed method can effectively improve the overall performance of vehicle control system and realize the coordination of various control objectives.

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3339 ◽  
Author(s):  
Zhao ◽  
Lu ◽  
Zhang

A Stackelberg game-based cooperative control strategy is proposed for enhancing the lateral stability of a four-wheel independently driving electric vehicle (FWID-EV). An upper‒lower double-layer hierarchical control structure is adopted for the design of a stability control strategy. The leader‒follower-based Stackelberg game theory (SGT) is introduced to model the interaction between two unequal active chassis control subsystems in the upper layer. In this model, the direct yaw-moment control (DYC) and the active four-wheel steering (AFWS) are treated as the leader and the follower, respectively, based on their natural characteristics. Then, in order to guarantee the efficiency and convergence of the proposed control strategy, a sequential quadratic programming (SQP) algorithm is employed to solve the task allocation problem among the distributed actuators in the lower layer. Also, a double-mode adaptive weight (DMAW)- adjusting mechanism is designed, considering the negative effect of DYC. The results of cosimulation with CarSim and Matlab/Simulink demonstrate that the proposed control strategy can effectively improve the lateral stability by properly coordinating the actions of AFWS and DYC.


Author(s):  
Avesta Goodarzi ◽  
Fereydoon Diba ◽  
Ebrahim Esmailzadeh

Basically, there are two main techniques to control the vehicle yaw moment. First method is the indirect yaw moment control, which works on the basis of active steering control (ASC). The second one being the direct yaw moment control (DYC), which is based on either the differential braking or the torque vectoring. An innovative idea for the direct yaw moment control is introduced by using an active controller system to supervise the lateral dynamics of vehicle and perform as an active yaw moment control system, denoted as the stabilizer pendulum system (SPS). This idea has further been developed, analyzed, and implemented in a standalone direct yaw moment control system, as well as, in an integrated vehicle dynamic control system with a differential braking yaw moment controller. The effectiveness of SPS has been evaluated by model simulation, which illustrates its superior performance especially on low friction roads.


Vehicles ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 127-144
Author(s):  
Andoni Medina ◽  
Guillermo Bistue ◽  
Angel Rubio

Direct Yaw Moment Control (DYC) is an effective way to alter the behaviour of electric cars with independent drives. Controlling the torque applied to each wheel can improve the handling performance of a vehicle making it safer and faster on a race track. The state-of-the-art literature covers the comparison of various controllers (PID, LPV, LQR, SMC, etc.) using ISO manoeuvres. However, a more advanced comparison of the important characteristics of the controllers’ performance is lacking, such as the robustness of the controllers under changes in the vehicle model, steering behaviour, use of the friction circle, and, ultimately, lap time on a track. In this study, we have compared the controllers according to some of the aforementioned parameters on a modelled race car. Interestingly, best lap times are not provided by perfect neutral or close-to-neutral behaviour of the vehicle, but rather by allowing certain deviations from the target yaw rate. In addition, a modified Proportional Integral Derivative (PID) controller showed that its performance is comparable to other more complex control techniques such as Model Predictive Control (MPC).


2018 ◽  
Vol 41 (9) ◽  
pp. 2428-2440 ◽  
Author(s):  
Jiaxu Zhang ◽  
Jing Li

This paper presents an integrated vehicle chassis control (IVCC) strategy to improve vehicle handling and stability by coordinating active front steering (AFS) and direct yaw moment control (DYC) in a hierarchical way. In high-level control, the corrective yaw moment is calculated by the fast terminal sliding mode control (FTSMC) method, which may improve the transient response of the system, and a non-linear disturbance observer (NDO) is used to estimate and compensate for the model uncertainty and external disturbance to suppress the chattering of FTSMC. In low-level control, the null-space-based control reallocation method and inverse tyre model are utilized to transform the corrective yaw moment to the desired longitudinal slips and the steer angle increment of front wheels by considering the constraints of actuators and friction ellipse of each wheel. Finally, the performance of the proposed control strategy is verified through simulations of various manoeuvres based on vehicle dynamic software CarSim.


2016 ◽  
pp. 631-636 ◽  
Author(s):  
T. Kobayashi ◽  
H. Sugiura ◽  
E. Ono ◽  
E. Katsuyama ◽  
M. Yamamoto

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