Integrated model predictive and torque vectoring control for path tracking of 4‐wheel‐driven autonomous vehicles

Ren, Yong; Zheng, Ling; Khajepour, Amir

doi:10.1049/iet-its.2018.5095

Cited by 56 publications

(38 citation statements)

References 27 publications

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“…Hu Jiaming et al designed a controller based on MPC for autonomous tracked vehicle and introduced feedback correction to deal with modeling error and disturbance [62]. Reference [63][64][65] proposed hierarchical control framework for path tracking based on MPC. Joseph Christian Gerdes et al established the stability constraint envelope and environmental road constraint envelope to solve the vehicle tracking and tracking stability control problem based on MPC [66][67][68].…”

Section: G Mpc Control Methodsmentioning

confidence: 99%

“…The SMC method has the advantages of fast response and insensitivity to parameter changes and disturbances. The vehicle control methods based on SMC are studied to deal with the problems of strong coupling, non-linearity, parameters uncertainty, and load transfer [93][94][95][96][97][98]. In order to deal with the non-linearity and failure of the steering system, the active fault-tolerant path tracking control method based on SMC is proposed and the influence of disturbance torque, time delay and noise on trajectory tracking control are analyzed [99][100].…”

Section: B Smc Control Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

Yao

Tian

Wang³

et al. 2020

IEEE Access

123

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Autonomous vehicle technology aims to improve driving safety, driving comfort, and its economy, as well as reduce traffic accident rate. As the basic part of autonomous vehicle motion control module, path tracking aims to follow the reference path accurately, ensure vehicle stability and satisfy the robust performance of the control system. This paper introduces the representative control strategies, robust control strategies and parameter observation-based control strategies on path tracking for autonomous vehicle. Furthermore, the implementations and disadvantages are summarized. Most importantly, the critical review in this paper provides a list and discussion of the remaining challenges and unsolved problems on path tracking control.

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Section: G Mpc Control Methodsmentioning

confidence: 99%

Section: B Smc Control Methodsmentioning

confidence: 99%

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

Yao

Tian

Wang³

et al. 2020

IEEE Access

123

View full text Add to dashboard Cite

show abstract

“…Conventionally, the repulsive potential of an obstacle is modelled as the function of its proximity to the EV [27, 30]. Obstacles should strongly repel the EV when they are in close proximity to prevent a collision.…”

Section: Literature Reviewmentioning

confidence: 99%

2‐dimensional human‐like driver model for autonomous vehicles in mixed traffic

Sharath

Velaga

Quddus

2020

IET intell. transp. syst

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Classical artificial potential approach of motion planning is extended for emulating human driving behaviour in two dimensions. Different stimulus parameters including type of ego‐vehicle, type of obstacles, relative velocity, relative acceleration, and lane offset are used. All the surrounding vehicles are considered to influence drivers' decisions. No emphasis is laid on vehicle control; instead, an ego vehicle is assumed to reach the desired state. The study is on human‐like driving behaviour modelling. The developed motion planning algorithm formulates repulsive and attractive potentials in a data‐driven way in contrast to the classical arbitrary formulation. Interaction between the stimulus parameters is explicitly considered by using multivariate cumulative distribution functions. Comparison of two‐dimensional (lateral and longitudinal) performance indicators with a baseline model and generative adversarial networks indicate the effectiveness and suitability of the developed motion planning algorithm in the mixed traffic environment.

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“…Significant modeling error could be caused if the linearized tire model is used without imposing constraints on the slip angle as the lateral tire forces will be saturated and no longer increase linearly with the slip angle when the lateral vehicle dynamics enter the nonlinear zone. As a result, the predicted states of the vehicle would deviate from actual values and therefore affect control accuracy [ 21 ]. Although adopting a nonlinear tire model contributes to minimizing the modeling error, it is still important for the slip angle to be contained within a small interval.…”

Section: Introductionmentioning

confidence: 99%

A Two-Layer Controller for Lateral Path Tracking Control of Autonomous Vehicles

Yin

Huang

2020

Sensors

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This paper presents a two-layer controller for accurate and robust lateral path tracking control of highly automated vehicles. The upper-layer controller, which produces the front wheel steering angle, is implemented with a Linear Time-Varying MPC (LTV-MPC) whose prediction and control horizon are both optimized offline with particle swarm optimization (PSO) under varying working conditions. A constraint on the slip angle is imposed to prevent lateral forces from saturation to guarantee vehicle stability. The lower layer is a radial basis function neural network proportion-integral-derivative (RBFNN-PID) controller that generates electric current control signals executable by the steering motor to rapidly track the target steering angle. The nonlinear characteristics of the steering system are modeled and are identified on-line with the RBFNN so that the PID controller’s control parameters can be adjusted adaptively. The results of CarSim-Matlab/Simulink joint simulations show that the proposed hierarchical controller achieves a good level of path tracking accuracy while maintaining vehicle stability throughout the path tracking process, and is robust to dynamic changes in vehicle velocities and road adhesion coefficients.

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Integrated model predictive and torque vectoring control for path tracking of 4‐wheel‐driven autonomous vehicles

Cited by 56 publications

References 27 publications

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

2‐dimensional human‐like driver model for autonomous vehicles in mixed traffic

A Two-Layer Controller for Lateral Path Tracking Control of Autonomous Vehicles

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