scholarly journals Design of Integrated Autonomous Driving Control System that Incorporates Chassis Controllers for Improving Path Tracking Performance and Vehicle Stability

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 144
Author(s):  
Taewon Ahn ◽  
Yongki Lee ◽  
Kihong Park
Keyword(s):  
Control System ◽  
Sliding Mode ◽  
Autonomous Vehicle ◽  
Safety Margin ◽  
Autonomous Driving ◽  
Path Tracking ◽  
Tracking Performance ◽  
Change Scenario ◽  
Vehicle Stability ◽  
Yaw Moment

This paper describes an integrated autonomous driving (AD) control system for an autonomous vehicle with four independent in-wheel motors (IWMs). The system consists of two parts: the AD controller and the chassis controller. These elements are functionally integrated to improve vehicle stability and path tracking performance. The vehicle is assumed to employ an IWM independently at each wheel. The AD controller implements longitudinal/lateral path tracking using proportional-integral(PI) control and adaptive model predictive control. The chassis controller is composed of two lateral control units: the active front steering (AFS) control and the torque vectoring (TV) control. Jointly, they find the yaw moment to maintain vehicle stability using sliding mode control; AFS is prioritized over TV to enhance safety margin and energy saving. Then, the command yaw moment is optimally distributed to each wheel by solving a constrained least-squares problem. Validation was performed using simulation in a double lane change scenario. The simulation results show that the integrated AD control system of this paper significantly improves the path tracking capability and vehicle stability in comparison with other control systems.

Vehicle Direct Yaw Moment Control with Longitudinal Forces Distribution

2014 ◽  
Vol 709 ◽  
pp. 331-334
Author(s):  
Man Hong Huang ◽  
Huan Shen ◽  
Yun Sheng Tan
Keyword(s):  
Control System ◽  
Reference Model ◽  
Sliding Mode ◽  
Stability Control ◽  
Yaw Rate ◽  
Vehicle Stability Control ◽  
Braking Torque ◽  
Stability Control System ◽  
Yaw Moment Control ◽  
Yaw Moment

In this paper, a vehicle stability control system is proposed to improve vehicle comfort, handling and stability. The control system includes reference model, DYC controller and Distributer. Reference model is used to obtain the desired yaw rate. DYC controller determines the desired yaw moment by means of sliding-mode technique. Distributer, based on maneuverability and comfort, distributes driving torque or braking torque according to the desired yaw rate. Simulation result shows that the proposed control algorithm can improve vehicle handling and stability effectively.

Torque vectoring control system for distributed drive electric bus under complicated driving conditions

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Liang Su ◽  
Zhenpo Wang ◽  
Chao Chen
Keyword(s):  
Control System ◽  
Feedback Control ◽  
Control Algorithm ◽  
Sliding Mode ◽  
Steering Wheel ◽  
Content Type ◽  
Yaw Rate ◽  
Torque Vectoring ◽  
Driving Conditions ◽  
Yaw Moment

Purpose The purpose of this study is to propose a torque vectoring control system for improving the handling stability of distributed drive electric buses under complicated driving conditions. Energy crisis and environment pollution are two key pressing issues faced by mankind. Pure electric buses are recognized as the effective method to solve the problems. Distributed drive electric buses (DDEBs) as an emerging mode of pure electric buses are attracting intense research interests around the world. Compared with the central driven electric buses, DDEB is able to control the driving and braking torque of each wheel individually and accurately to significantly enhance the handling stability. Therefore, the torque vectoring control (TVC) system is proposed to allocate the driving torque among four wheels reasonably to improve the handling stability of DDEBs. Design/methodology/approach The proposed TVC system is designed based on hierarchical control. The upper layer is direct yaw moment controller based on feedforward and feedback control. The feedforward control algorithm is designed to calculate the desired steady-state yaw moment based on the steering wheel angle and the longitudinal velocity. The feedback control is anti-windup sliding mode control algorithm, which takes the errors between actual and reference yaw rate as the control variables. The lower layer is torque allocation controller, including economical torque allocation control algorithm and optimal torque allocation control algorithm. Findings The steady static circular test has been carried out to demonstrate the effectiveness and control effort of the proposed TVC system. Compared with the field experiment results of tested bus with TVC system and without TVC system, the slip angle of tested bus with TVC system is much less than without TVC. And the actual yaw rate of tested bus with TVC system is able to track the reference yaw rate completely. The experiment results demonstrate that the TVC system has a remarkable performance in the real practice and improve the handling stability effectively. Originality/value In view of the large load transfer, the strong coupling characteristics of tire , the suspension and the steering system during coach corning, the vehicle reference steering characteristics is defined considering vehicle nonlinear characteristics and the feedforward term of torque vectoring control at different steering angles and speeds is designed. Meanwhile, in order to improve the robustness of controller, an anti-integral saturation sliding mode variable structure control algorithm is proposed as the feedback term of torque vectoring control.

Research on Vehicle Stability Based on DYC and AFS Integrated Controller

2013 ◽  
Vol 278-280 ◽  
pp. 1510-1515 ◽  
Author(s):  
Jie Tian ◽  
Ya Qin Wang ◽  
Ning Chen
Keyword(s):  
Control Method ◽  
Sliding Mode ◽  
Stability Control ◽  
Front Wheel ◽  
Vehicle Stability ◽  
Sideslip Angle ◽  
Stable Domain ◽  
Vehicle Stability Control ◽  
Integrated Controller ◽  
Yaw Moment

A new vehicle stability control method integrated direct yaw moment control (DYC) with active front wheel steering (AFS) was proposed. On the basis of the vehicle nonlinear model, vehicle stable domain was determined by the phase plane of sideslip angle and sideslip angular velocity. When the vehicle was outside the stable domain, DYC was firstly used to produce direct yaw moment, which can make vehicle inside the stable domain. Then AFS sliding mode control was used to make the sideslip angle and yaw rate track the reference vehicle model. The simulation results show that the integrated controller improves vehicle stability more effectively than using the AFS controller alone.

Improvement in the vehicle stability of distributed-drive electric vehicles based on integrated model-matching control

2017 ◽  
Vol 232 (3) ◽  
pp. 341-356 ◽  
Author(s):  
Xudong Zhang ◽  
Dietmar Göhlich
Keyword(s):  
Electric Vehicles ◽  
Sliding Mode ◽  
Integrated Model ◽  
Recursive Least Squares ◽  
Adaptive Sliding Mode Control ◽  
Vehicle Stability ◽  
Sliding Mode Controller ◽  
Model Matching ◽  
Vehicle Motion ◽  
Yaw Moment

This paper presents a vehicle dynamic stability controller for distributed-drive electric vehicles. A hierarchical control structure is adopted for the proposed controller. An upper controller is designed on the basis of integrated model-matching control. It consists of a feedforward component plus a feedback component to calculate the desired external yaw moment to achieve the desired vehicle motion. The feedforward control aims at compensating the effect caused by the variation in the linear cornering stiffnesses of the tyres during the life cycle of the tyres. It provides a rapid response under common driving conditions. The linear cornering stiffnesses of the tyres are estimated in real time by the adaptive forgetting-factor recursive least-squares method. Since many vehicle parameters have strongly non-linear and time-varying characteristics, adaptive sliding mode control is used as the feedback component to make the controller robust against systematic uncertainties. To combine the outputs of feedforward and feedback together and to avoid probable conflict, a weight gain coefficient is obtained. Additionally, a conventional sliding-mode controller is introduced as a comparative upper control strategy. The lower controller is utilized to allocate the required yaw moment and traction to the four independent motors, taking into account the tyre grip margins. Simulations for a low- g manoeuvre and a high- g manoeuvre are carried out to evaluate the proposed control algorithm. The results show that the proposed vehicle stability controller can significantly stabilize the vehicle motion and greatly reduce the driver’s workload in comparison with with the conventional sliding-mode controller.

scholarly journals Adaptive Coordinated Path Tracking Control Strategy for Autonomous Vehicles with Direct Yaw Moment Control

2022 ◽  
Vol 35 (1) ◽  
Author(s):  
Ying Tian ◽  
Qiangqiang Yao ◽  
Peng Hang ◽  
Shengyuan Wang
Keyword(s):  
Control Strategy ◽  
Autonomous Vehicles ◽  
Tracking Control ◽  
High Speed ◽  
Front Wheel ◽  
Path Tracking ◽  
Vehicle Stability ◽  
Tracking Accuracy ◽  
Large Curvature ◽  
Yaw Moment

AbstractIt is a striking fact that the path tracking accuracy of autonomous vehicles based on active front wheel steering is poor under high-speed and large-curvature conditions. In this study, an adaptive path tracking control strategy that coordinates active front wheel steering and direct yaw moment is proposed based on model predictive control algorithm. The recursive least square method with a forgetting factor is used to identify the rear tire cornering stiffness and update the path tracking system prediction model. To adaptively adjust the priorities of path tracking accuracy and vehicle stability, an adaptive strategy based on fuzzy rules is applied to change the weight coefficients in the cost function. An adaptive control strategy for coordinating active front steering and direct yaw moment is proposed to improve the path tracking accuracy under high-speed and large-curvature conditions. To ensure vehicle stability, the sideslip angle, yaw rate and zero moment methods are used to construct optimization constraints based on the model predictive control frame. It is verified through simulation experiments that the proposed adaptive coordinated control strategy can improve the path tracking accuracy and ensure vehicle stability under high-speed and large-curvature conditions.

Vehicle handling and stability control by the cooperative control of 4WS and DYC

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740090 ◽  
Author(s):  
Huan Shen ◽  
Yun-Sheng Tan
Keyword(s):  
Control System ◽  
Cooperative Control ◽  
Sliding Mode ◽  
Integrated Control ◽  
Longitudinal Velocity ◽  
Stability Control ◽  
Sideslip Angle ◽  
Vehicle Handling ◽  
Braking Torque ◽  
Yaw Moment

This paper proposes an integrated control system that cooperates with the four-wheel steering (4WS) and direct yaw moment control (DYC) to improve the vehicle handling and stability. The design works of the four-wheel steering and DYC control are based on sliding mode control. The integration control system produces the suitable 4WS angle and corrective yaw moment so that the vehicle tracks the desired yaw rate and sideslip angle. Considering the change of the vehicle longitudinal velocity that means the comfort of driving conditions, both the driving torque and braking torque are used to generate the corrective yaw moment. Simulation results show the effectiveness of the proposed control algorithm.

scholarly journals A Model Predictive Controller with Longitudinal Speed Compensation for Autonomous Vehicle Path Tracking

2019 ◽  
Vol 9 (22) ◽  
pp. 4739 ◽  
Author(s):  
Yao ◽  
Tian
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Autonomous Vehicle ◽  
Tracking Error ◽  
Path Tracking ◽  
Vehicle Stability ◽  
Tracking Accuracy ◽  
Prediction Horizon ◽  
Tracking Model ◽  
Vehicle Path

Autonomous vehicle path tracking accuracy faces challenges in being accomplished due to the assumption that the longitudinal speed is constant in the prediction horizon in a model predictive control (MPC) control frame. A model predictive control path tracking controller with longitudinal speed compensation in the prediction horizon is proposed in this paper, which reduces the lateral deviation, course deviation, and maintains vehicle stability. The vehicle model, tire model, and path tracking model are described and linearized using the small angle approximation method and an equivalent cornering stiffness method. The mechanism of action of longitudinal speed changed with state vector variation, and the stability of the path tracking closed-loop control system in the prediction horizon is analyzed in this paper. Then the longitudinal speed compensation strategy is proposed to reduce tracking error. The controller designed was tested through simulation on the CarSim-Simulink platform, and it showed improved performance in tracking accuracy and satisfied vehicle stability constrains.

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