Modular integrated longitudinal and lateral vehicle stability control for electric vehicles

Nahidi, Asal; Kasaiezadeh, Alireza; Khosravani, Saeid; Khajepour, Amir; Chen, Shih-Ken; Litkouhi, Bakhtiar

doi:10.1016/j.mechatronics.2017.04.001

Cited by 86 publications

(36 citation statements)

References 14 publications

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“…The path tracking model is shown in Figure 5, illustrating the relationship between the lateral deviation e, the heading deviation θ e , and the distance s along the path. In most of the existing path-tracking controllers, the lateral deviation e, the heading deviation θ e are chosen as the reference states [28,29,32,33], solving the optimization problem by minimizing e and θ e . However, path tracking lateral deviation is minimized when vehicle sideslip is held tangent to the desired path at all times [19,20,37].…”

Section: Path-tracking Modelmentioning

confidence: 99%

“…Existing research has held the assumption that the vehicle longitudinal speed is a constant in prediction horizon [25][26][27][28][29], so that the optimization problem becomes convex optimization; thus it becomes easy for the optimization problem to obtain its solution that satisfies the constraints. In fact, the speed of the vehicle is constantly changing, which will produce corresponding error during predicting vehicle states in prediction horizon.…”

Section: Mpc Controller Design and Longitudinal Speed Compensationmentioning

confidence: 99%

“…Model predictive control (MPC) has the capability of handling system constraints and future prediction in the design process. It minimizes the gap between the reference path and the actual path by the vehicle dynamics model in a prediction horizon, and it has become a popular method in the control of autonomous vehicles [26][27][28][29][30]. A differential evolution algorithm was introduced into the MPC path tracking controller in order to improve the computational efficiency [31].…”

Section: Introductionmentioning

confidence: 99%

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

Yao

Tian

2019

Applied Sciences

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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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Section: Path-tracking Modelmentioning

confidence: 99%

Section: Mpc Controller Design and Longitudinal Speed Compensationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Yao

Tian

2019

Applied Sciences

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“…Ref. [25] proposed a two-layer model predictive control controller to optimize the required longitudinal force and yaw moment adjustments and to achieve the minimized error of the steady state tracking objective. A combined control algorithm was designed in [26] by taking the yaw rate and the centroid slip angle error as input variables and using the braking torque as the steering angle of the control objectives.…”

Section: Introductionmentioning

confidence: 99%

Novel Design and Lateral Stability Tracking Control of a Four-Wheeled Rollator

Zhang

et al. 2019

Applied Sciences

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Design and control of smart rollators have attracted increasing research interests in the past decades. To meet the requirements of the elderly or disabled users, this paper proposes a novel design and tracking control scheme for empowering and assisting natural human mobility with a four-wheeled rollator. Firstly, by integrating the advantages of Kano Model Analysis and the Theory of Inventive Problem Solving (TRIZ), we introduce a novel Kano-TRIZ industrial design method to design and optimize its mechanical structure. The demand and quality characteristics of the clinical rollator are analyzed according to the Kano model. The Quality Function Deployment (QFD) and TRIZ are adopted to integrate industrial product innovations and optimize the function configuration. Furthermore, a lateral stability controller based on Model Predictive Control (MPC) scheme is introduced to achieve good tracking control performance with the lateral deviation and the heading angle deviation. Finally, the feasibility of the design and control method is verified with a simulation study. The simulation results indicate that the proposed algorithm keeps the lateral position error in a reasonable range. In the co-simulation of ADAMS-MATLAB, the trajectory of the rollator is smooth with constrained position error within 0.1 m, the turning angle and speed can achieve stable tracking control within 5 s and the heading angle is accurate and the speed is stable. A compared experiment with MPC and SMC show that MPC controller has faster response, higher tracking accuracy and smoother trajectory on the novel designed rollator. With the increasing demand for rollators in the global market, the methodology proposed in this paper will attract more research and industry interests.

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“…Even though variable torque distribution of the distributed‐drive EV was discussed for multiobjective coordination, multiobjective optimization for integrated vehicle dynamics management of the distributed‐drive EV with AFS is still a new research area in EV technology. As shown in Figure , an integrated vehicle dynamics management method of the distributed‐drive EV with AFS is studied in this paper to coordinate the commands for vehicle lateral dynamics, longitudinal dynamics, and energy saving control from the top to lower layer to regulate the steering system, traction system, and braking system.…”

Section: Introductionmentioning

confidence: 99%

Integrated vehicle dynamics management for distributed‐drive electric vehicles with active front steering using adaptive neural approaches against unknown nonlinearity

Huang

Wong

2019

Intl J Robust & Nonlinear

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Summary This paper proposes a new integrated vehicle dynamics management for enhancing the yaw stability and wheel slip regulation of the distributed‐drive electric vehicle with active front steering. To cope with the unknown nonlinear tire dynamics with uncertain disturbances in integrated control problem of vehicle dynamics, a neuro‐adaptive predictive control is therefore proposed for multiobjective coordination of constrained systems with unknown nonlinearity. Unknown nonlinearity with unmodeled dynamics is modeled using a random projection neural network via adaptive machine learning, where a new adaptation law is designed in premise of Lyapunov stability. Given the computational efficiency, a neurodynamic method is extended to solve the constrained programming problem with unknown nonlinearity. To test the performance of the proposed control method, simulations were conducted using a validated vehicle model. Simulation results show that the proposed neuro‐adaptive predictive controller outperforms the classical model predictive controller in tracking nominal wheel slip ratio, desired vehicle yaw rate and sideslip angle, showing its significance in vehicle yaw stability enhancement and wheels slip regulation.

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Modular integrated longitudinal and lateral vehicle stability control for electric vehicles

Cited by 86 publications

References 14 publications

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

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

Novel Design and Lateral Stability Tracking Control of a Four-Wheeled Rollator

Integrated vehicle dynamics management for distributed‐drive electric vehicles with active front steering using adaptive neural approaches against unknown nonlinearity

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