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

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.

Direct yaw-moment control of vehicles based on phase plane analysis

In view of the problems related to vehicle-handling stability and the real-time correction of the heading direction, nonlinear analysis of a vehicle steering system was carried out based on phase plane theory. Subsequently, direct yaw-moment control (DYC) of the vehicle was performed. A four-wheel, seven-degree-of-freedom nonlinear dynamic model that included the nonlinear characteristics of the tire was established. The stable and unstable regions of the vehicle phase plane were divided, and the stable boundary model was established by analyzing the side slip angle–yaw rate ([Formula: see text]) and side slip angle–side slip angle rate [Formula: see text] phase planes as functions of the vehicle state variables. In the unstable region of the phase plane, taking the instability degree as the control target, a fuzzy neural network control strategy was utilized to determine the additional yawing moment of the vehicle required for stability restoration, which pulled the vehicle back from an unstable state to the stable region. In the stable region of the phase plane, a fuzzy control strategy was utilized to determine the additional yawing moment so that the actual state variables followed the ideal state variables. In this way, the vehicle responded rapidly and accurately to the steering motion of the driver. A simulation platform was established in MATLAB/Simulink and three working condition was tested, that is, step, sine with dwell, and sine amplification signals. The results showed that the vehicle handling stability and the instantaneous heading-direction adjustment ability were both improved due to the control strategy.

Coordination control of active front steering and direct yaw moment control based on stability judgment for AVs stability enhancement

A coordination control strategy based on stability judgment is presented for autonomous vehicles (AVs) aiming to enhance the handling and stability performance. Firstly, the stability judgment scheme is used to evaluate the real-time stability level of vehicles based on the Self-Organizing Feature Map (SOFM) neural network and K-Means algorithm. Secondly, a coordination controller of active front steering (AFS) and direct yaw moment control (DYC) is designed to track the desired vehicle motion. To enhance the handling and stability of AVs, the weights of AFS and DYC controllers are adaptively adjusted according to the vehicle stability level. Finally, the effectiveness of the proposed method is verified in co-simulation environment of CarSim and Simulink, and a rapid control prototyping test is implemented to evaluate the feasibility and robustness. The results indicate that the stability judgment scheme and coordination control strategy for AVs can not only satisfy the requirements of path tracking accuracy but also enhance the handling and stability performance.

Vehicle direct yaw moment control system based on the improved linear quadratic regulator

Purpose This study aims to propose an improved linear quadratic regulator (LQR) based on the adjusting weight coefficient, which is used to improve the performance of the vehicle direct yaw moment control (DYC) system. Design/methodology/approach After analyzing the responses of the side-slip angle and the yaw rate of the vehicle when driving under different road adhesion coefficients, the genetic algorithm and fuzzy logic theory were applied to design the parameter regulator for an improved LQR. This parameter regulator works according to the changes in the road adhesion coefficient between the tires and the road. Hardware-in-the-loop (HiL) tests with double-lane changes under low and high road surface adhesion coefficients were carried out. Findings The HiL test results demonstrate the proposed controllers’ effectiveness and reasonableness and satisfy the real-time requirement. The effectiveness of the proposed controller was also proven using the vehicle-handling stability objective evaluation method. Originality/value The objective evaluation results reveal better performance using the improved LQR DYC controller than a front wheel steering vehicle, especially in reducing driver fatigue, improving vehicle-handling stability and enhancing driving safety.

Comparison of Typical Controllers for Direct Yaw Moment Control Applied on an Electric Race Car

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).

Comparison of Typical Controllers for Direct Yaw Moment Control Applied on an Electric Race Car

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 andfaster 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, more advanced comparison on important characteristics of the controllers performance is missed, 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 PID controller showed that its performance is comparable to other more complex control techniques such as MPC.