Adaptive integrated control design for vehicle dynamics using phase-plane analysis

Azadi, Shahram; Naghibian, Masoud; Kazemi, Reza

doi:10.1007/s12206-015-0544-9

Cited by 11 publications

(3 citation statements)

References 9 publications

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“…We used two important parameters, yaw rate g and sideslip angle b, to characterize the vehicle stability based on the b À g phase plane. The phase plane analysis method is commonly applied for similar purposes, such as the b À _ b phase plane representing the sideslip angle and its changing trend, 21 or the b À g phase plane representing the sideslip angle and yaw rate. 22,23 The second method is particularly well applicable to investigating vehicle stability, because it includes two distinct parameters that intuitively characterize the stability.…”

Section: Simulation Resultsmentioning

confidence: 99%

Development and evaluation of torque distribution methods for four-wheel independent-drive electric vehicle based on parameter estimation

Chen

Zhang

Bao

et al. 2019

Advances in Mechanical Engineering

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A hierarchical controller including a vehicle condition parameter estimator for improving the vehicle stability is theoretically applied to a four-wheel independent-drive electric vehicle. This study is aimed at combining the parameters estimation with vehicle stability control and evaluating different optimization algorithms based on the computation time and fluctuation. The hierarchical controller consists of a vehicle condition parameter estimator, active yaw moment controller, and torque distribution controller. The vehicle condition parameter estimator is based on the Unscented Kalman Filter, which avoids the truncation error caused by Taylor expansion to convert a nonlinear system into a linear system. The active yaw moment controller is designed based on the sliding mode control. The controller employs co-control between the vehicle sideslip angle and yaw rate, which properly accounts for both factors. The objective function of the torque distribution controller is based on the tire utilization function and constraint condition. The torque distribution and wheel slip rate are accounted for simultaneously. The torque distribution methods are tested based on nonlinear programming, quadratic programming, and weighted least squares for the sake of comparison. The simulation results demonstrate the accuracy of the proposed vehicle state estimator and the effectiveness of the proposed stability controller.

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Section: Simulation Resultsmentioning

confidence: 99%

Development and evaluation of torque distribution methods for four-wheel independent-drive electric vehicle based on parameter estimation

Chen

Zhang

Bao

et al. 2019

Advances in Mechanical Engineering

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show abstract

“…The nonlinear vehicle stability can be analyzed by the phase-plane method. 36,37 This method is an effective tool for considering vehicle system stability, and can reflect the stability of the vehicle during the under-steering or over-steering maneuvers. Furthermore, the sideslip angle and the time derivative of this angle in the phase-plane are depicted in Figure 15.…”

Section: Constraints Of Analysismentioning

confidence: 99%

Path-tracking of an autonomous vehicle via model predictive control and nonlinear filtering

Cui

Ding

Zhou

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part D:

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To improve the stability of the autonomous vehicle for high speed tracking, a vehicle estimator scheme integrated into a path-tracking system has been proposed in this paper. Vehicle stability is related to road condition (low road adhesion, high road adhesion, and changing road adhesion) and vehicle state, thus a state observer has been preferred in this paper to estimate vehicle state and tire-road friction as a means of judging vehicle stabilization. For the approach to the estimation, an unscented Kalman filter (UKF) employing a three degrees-of-freedom vehicle model combined with a Magic Formula (MF) tire model was designed. As a widely used model control method, the multi-constraints model predictive control (MMPC) was proposed and that was then used to calculate the desired front steering angle for tracking the planned path. The performance of the MMPC controller, with the estimator, was evaluated by the vehicle simulation software CARSIM and Matlab/Simulink. The simulation results show that the designed MMPC controller with the estimator successfully performs path-tracking at high speed for the intelligent vehicle.

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“…Stability boundaries and the partition of the controllable regions were investigated by Bobier et al 7 and Jin et al, 8 which provided the basis for the regional control. Based on the above research, Azadi et al 9 designed a vehicle steering stability controller using phase plane method, including manoeuvrability control and stability control. Simulation results show that considerable improvements can be achieved in vehicle dynamic performance through the adaptive integrated control.…”

Section: Introductionmentioning

confidence: 99%

Research on vehicle critical condition prediction and optimal yaw moment intervention based on nonlinear dynamics and Monte Carlo simulation

Lou

2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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Timely and effective yaw moment intervention is required to suppress the instability tendency of vehicle under critical conditions, which is mainly caused by the overshoot of state parameters. As the development of vehicle nonlinear dynamics, the prediction of vehicle critical condition enables the vehicle to stabilize in this condition by timely yaw moment intervention. The yaw moment intervention law based on the prediction of critical conditions cannot be solely investigated by the initial motion states, since the online iterative optimization is an indispensable part in the predictive control. In this paper, the combination of threshold model, quadratic optimal yaw moment prediction method and Monte Carlo simulation provides a new approach to investigate the optimal open loop yaw moment intervention law based on initial motion states, which can be used to maintain the stability of vehicle plane motion in critical situations and optimize the predictive control method. Specifically, a total of 480,000 samples of quadratic yaw moment intervention parameters and corresponding system responses are obtained and analysed. It is certified that the optimized quadratic yaw moment can effectively suppress the overshoot of system responses. Specifically, statistics indicate that the initial value of yaw moment intervention plays an important role in preventing instability, but with the increase of speed and steering angle, the final yaw moment will be the decisive factor to maintain the lateral stability.

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Adaptive integrated control design for vehicle dynamics using phase-plane analysis

Cited by 11 publications

References 9 publications

Development and evaluation of torque distribution methods for four-wheel independent-drive electric vehicle based on parameter estimation

Development and evaluation of torque distribution methods for four-wheel independent-drive electric vehicle based on parameter estimation

Path-tracking of an autonomous vehicle via model predictive control and nonlinear filtering

Research on vehicle critical condition prediction and optimal yaw moment intervention based on nonlinear dynamics and Monte Carlo simulation

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