Path planning and stability control of collision avoidance system based on active front steering

Wnag, ChunYan; Zhao, Wanzhong; Xu, Zhiheng; Guan, Zailin

doi:10.1007/s11431-016-9016-1

Cited by 45 publications

(32 citation statements)

References 10 publications

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“…Although the problem of driving's terminal time is free can be solved by BVP4C solution, but if the initial evaluated value is not rational, then the results may be not convergence. So, the trajectory's initial weight set W INT (i.e., [1,1,1,3]) should be evaluated, ACT-R cognitive model is the core of the ACT-R initial module, the information of driving region boundary of the vehicle, goal, and constraints can be encoded and changed into some needed information that can generate trajectory and initial weight set W INT , if the constraints are not exit, then the weight value W Default (i.e., [1,1,2,5]) is supposed as the default weight set.…”

Section: Act-r Initialized Behavior Modelingmentioning

confidence: 99%

“…The weight ratio value W 3 /W 1 is not accurate and it is the exponential form base 2: 2 −3 , 2 −2 , 2 −1 , 2 0 , 2 1 , 2 2 , 2 3 . Because human's cognition behavior is vague, so the accuracy of the weight ratio value does not affect the algorithm's results.…”

Section: The Act-r Weight Adjustment Behavior Modelingmentioning

confidence: 99%

“…Electric vehicle active collision avoidance system is one of the most effective methods to solve the rear-end accidents and it is a driving assistant system that is based on vehicle active safety technology. It is effective to improve driving safety by installing some sensors in the car [3,4]. We can know the environment and status with the sensors to assist driver driving, for example, avoiding obstacles and preventing accidents from happening actively.…”

Section: Introductionmentioning

confidence: 99%

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ACT-R Cognitive Model Based Trajectory Planning Method Study for Electric Vehicle’s Active Obstacle Avoidance System

Li¹,

Zhao

Xibo³

et al. 2018

Energies

Self Cite

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Abstract:The active obstacle avoidance system is one of the important components of the electric vehicle active safety system. In order to realize the active obstacle avoidance system driving the vehicle smoothly and without collision in complex road situation, a new dynamical trajectory planning method based on ACT-R (Adaptive Control of Thought-Rational) cognitive model is introduced. Firstly, the ACT-R cognitive architecture is introduced and the trajectory planning method's framework structure based on ACT-R cognitive model is built. Secondly, the modeling method of ACT-R cognitive model is introduced, the main module of ACT-R cognitive model includes the initialized behavior module, trajectory planning module, estimated behavioral module, and weight adjustment behavior module. Finally, the verification of the trajectory planning method is conducted by the simulation and experiment results. The simulation and experiment results showed that the method of AR (ACT-R) is effective and feasible. The AR method is better than the methods that are based on the OC (Optimal Control) and FN (fuzzy neural network fusion); this paper's method has more human behavior characteristics and can meet the demand of different constraints.

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Section: Act-r Initialized Behavior Modelingmentioning

confidence: 99%

Section: The Act-r Weight Adjustment Behavior Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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ACT-R Cognitive Model Based Trajectory Planning Method Study for Electric Vehicle’s Active Obstacle Avoidance System

Li¹,

Zhao

Xibo³

et al. 2018

Energies

Self Cite

View full text Add to dashboard Cite

show abstract

“…The past decades have witnessed many researches in trajectory tracking [4][5][6]. Many control structures have been proposed, which are mainly based on EPS (electronic power steering) [7], AFS (active steering system) [8], or SBW (steer by system) [9]. A model predictive controller (MPC) is presented for solving the path-tracking problem of terrestrial autonomous vehicles that can better match the target criteria.…”

Section: Introductionmentioning

confidence: 99%

A Trajectory Tracking Control Strategy of 4WIS/4WID Electric Vehicle with Adaptation of Driving Conditions

Zheng

Yang

2019

Applied Sciences

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The Four Wheel Independent Steering/Driving (4WIS/4WID) electric vehicle has the advantage that the rotation angle and driving torque of each wheel can be independently and accurately controlled. In this paper, a trajectory tracking strategy based on the hierarchical control method is designed. In the path tracking layer, the nonlinear state feedback controller is used, and the neural network Proportion Integration Differentiation (NNPID) controller is designed to track the desired path and to obtain the desired yaw rate. By tracking the desired yaw rate and vehicle speed, the terminal sliding mode controller in vehicle dynamics control layer calculates the desired resultant tire force. In the tire force distribution layer, the multiple optimization objectives, including vehicle stability performance objective, energy-saving performance objective, and tire wear energy consumption objectives are determined and the weight coefficient is adaptive to different working conditions based on fuzzy logic theory. Finally, the wheel steering angle and driving torque of each wheel are calculated by the nonlinear three-degree-of-freedom vehicle model. Simulation results show that it realizes the adaptive control of tire force while tracking the desired trajectory, improves the stability and energy saving of the vehicle, and effectively reduces tire wear.

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“…Many researchers have deeply studied various yaw stability control methods based on AFS for the vehicle. [6][7][8][9][10][11][12][13] Zhao proposed a CAS for autonomous vehicle based on AFS, and designed an AFS robust tracking controller to improve path tracking performance in Wnag et al 7 Zhao designed a H N controller for AFS to ensure that the steering wheel torque accurately and quickly tracks the reference torque, and controllability, stability, and steering portability of a vehicle with AFS can be improved, as presented in Zhao et al 8 The proportion-integration (PI), 6 proportion-integration-differentiation (PID), and fuzzy-PID control 9 methods were used for controller design of AFS to implement to track ideal yaw rate in the presence of model and parameter uncertainties and external perturbations to enhance yaw stability in emergencies. An AFS robust controller using a normalized left coprime factorization model is proposed in Ji et al 10 As reported in Song, 11 considering the AFS system of BMW double planetary gear sets as the research object, Song proposed a sliding mode control method for AFS, which realizes the stability control of the vehicle in the tracking reference yaw rate.…”

Section: Introductionmentioning

confidence: 99%

Design of an active front steering system for a vehicle using an active disturbance rejection control method

Sang

Chen

2019

Science Progress

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A linear vehicle model is commonly employed in the controller design for an active front steering (AFS). However, this simplified model has a considerable influence on the accuracy of the controller. In this article, an AFS controller using an active disturbance rejection control (ADRC) technique is proposed to prevent this problem. The AFS controller was established in MATLAB/Simulink to control the CarSim vehicle model for verification of the simulation. Under the straight-line driving disturbance condition, proportion-integration-differentiation (PID) control and ARDC substantially decreased with respect to the uncontrolled lateral offset and ADRC performed better than PID control. Under the double lane change (DLC) test working condition, the tracking error of the path, yaw rate, roll angle, and lateral acceleration, and error of the driving direction were used to evaluate the vehicle’s controllability and stability. These evaluation indexes were substantially improved by PID control and ADRC; similarly, ADRC was better than PID control. The tracking error of the ADRC in the presence of parameter variance and external disturbance was significantly smaller than that of PID control. The results have verified that the AFS controller based on ADRC can significantly improve vehicle controllability and stability.

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Path planning and stability control of collision avoidance system based on active front steering

Cited by 45 publications

References 10 publications

ACT-R Cognitive Model Based Trajectory Planning Method Study for Electric Vehicle’s Active Obstacle Avoidance System

ACT-R Cognitive Model Based Trajectory Planning Method Study for Electric Vehicle’s Active Obstacle Avoidance System

A Trajectory Tracking Control Strategy of 4WIS/4WID Electric Vehicle with Adaptation of Driving Conditions

Design of an active front steering system for a vehicle using an active disturbance rejection control method

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