Vehicle direct yaw moment control system based on the improved linear quadratic regulator

Xie, Xianyi; Jin, Lisheng; Guo, Baofeng; Shi, Jinjin

doi:10.1108/ir-08-2020-0168

Cited by 7 publications

(5 citation statements)

References 21 publications

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“…Simultaneously, particles with high fitness values were selected, and the GA algorithm was utilized for cross mutation to generate a new set of individuals for these particles. Subsequently, the velocity and position of each particle were updated using the PSO velocity and position update function, as indicated in Equation (22). Moreover, the global optimal solution was updated, and iteration continued until the predefined stopping conditions were satisfied.…”

Section: Lqr Based On Ga-pso Optimizationmentioning

confidence: 99%

“…Wang et al [ 21 ] proposed a gain scheduling robust linear-quadratic regulator (RLQR) that addressed the limitations of parameter uncertainty by adding an additional control term to the feedback contribution of the conventional LQR. Xie et al [ 22 ] introduced an enhanced LQR based on adjusted weight coefficients, employing fuzzy rules to modify the weight coefficients of the LQR and enhance the performance of the vehicle’s direct yaw torque control system. Experiments conducted in [ 23 ] demonstrated that utilizing a Genetic Algorithm (GA) to determine the parameters of the LQR resulted in better control effects compared to those obtained through the trial and error method.…”

Section: Introductionmentioning

confidence: 99%

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Research on Yaw Stability Control Strategy for Distributed Drive Electric Trucks

Gao,

Zhao,

Zhang

2023

Sensors

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With the advancement of vehicle electrification and intelligence, distributed drive electric trucks have emerged as the preferred choice for heavy-duty electric trucks. However, the control of yaw stability remains a significant issue. To tackle this concern, this study introduces a layered control strategy for yaw moment. Specifically, the upper layer utilizes a yaw moment controller based on linear quadratic regulator (LQR) to compute the additional yaw moment required. Additionally, in order to enhance the performance of the yaw moment controller, the weight matrix in LQR is optimized using a hybrid Genetic Algorithm and Particle Swarm Optimization algorithm (GA-PSO). The lower layer consists of a torque distribution layer, which establishes an objective function for minimizing tire utilization rate. Quadratic Programming algorithm is then employed to compute the optimal torque distribution value, thereby improving the vehicle’s stability. Subsequently, the stability control effects of the vehicle are simulated and compared on the Matlab/Simulink Trucksim joint simulation platform using four control strategies: the proposed control strategy, SMC, LQR, and without yaw moment control. These simulations are conducted under two working conditions: serpentine and double lane change. The results demonstrate that the proposed approach reduces the average yaw rate by 14.4%, 19.6%, and 42.15% while optimizing the average sideslip angle by 25.9%, 24.8%, and 52.3% in comparison to the other three control strategies. Consequently, the proposed control strategy significantly enhances the driving stability of the vehicle. Furthermore, the optimized allocation method reduces the average tire utilization rate by 42.6% in contrast to the average allocation method, thereby improving the stability control margin of the vehicle. These findings successfully validate the efficiency of the yaw stability control strategy presented in this article.

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Section: Lqr Based On Ga-pso Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Yaw Stability Control Strategy for Distributed Drive Electric Trucks

Gao,

Zhao,

Zhang

2023

Sensors

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“…Hu et al introduced an output constraint controller to address the path-following issue in autonomous vehicles equipped with four-wheel independent driving, aiming to maintain lateral stability through an adaptive linear quadratic regulator [18]. There are different kinds of control methods, such as linear quadratic regulator [19][20][21], fuzzy control [22,23], fuzzy PID control [24,25], and sliding mode control [26,27]. Liu et al achieved smooth adjustment of vehicle longitudinal-lateral motion under various conditions by controlling the in-wheel motor drive system [28].…”

Section: State Of the Artmentioning

confidence: 99%

“…By differential transformation, it is easy to obtain Equation (20). The exponential reaching rate is selected to constrain the trajectory of the system, as shown in Equation (21). The additional yaw moment can be obtained as Equation (22).…”

Section: Sliding Mode Controllermentioning

confidence: 99%

Enhancing Autonomous Vehicle Stability through Pre-Emptive Braking Control for Emergency Collision Avoidance

Lai,

Wang

2023

Applied Sciences

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A pre-emptive braking control method is proposed to improve the stability of autonomous vehicles during emergency collision avoidance, aiming to imitate the realistic human driving experience. A linear model predictive control is used to derive the front wheel steering angle to track a predefined fifth-degree polynomial trajectory. Based on a two-degrees-of-freedom (DOF) vehicle dynamics model, the maximum stable vehicle speed during collision avoidance can be determined. If the actual vehicle speed exceeds the maximum stable vehicle speed, braking action will be applied to the vehicle. Furthermore, four-wheel steering (4WS) control and direct yaw moment control (DYC) are employed to further improve the stability of the vehicle during collision avoidance. Simulation results under a double lane change scenario demonstrate that the control system incorporating pre-emptive braking, 4WS, and DYC can enhance the vehicle stability effectively during collision avoidance. Compared to the 2WS system without pre-emptive braking control, the maximum stable vehicle speed of the integrated control system can be increased by at least 56.9%. The proposed integrated control strategy has a positive impact on the safety of autonomous vehicles, and it can also provide reference for the research and development of autonomous driving systems.

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“…For electric vehicles equipped with in-wheel motors and steer-by-wire technology, the optimal coordination of active steering and direct yaw moment is investigated (Dong et al , 2023; Zhang et al , 2022). A linear quadratic regulator is designed based on the measured road adhesion coefficient in the underlying control, enhancing the effectiveness of the vehicle’s direct yaw moment control system (Xie et al , 2021). A quadratic programming active set approach is proposed to optimize the distributed additional yaw moment while minimizing tire adhesion utilization, considering the constraints imposed by the motor and braking systems’ operating characteristics (Min et al , 2021).…”

Section: Introductionmentioning

confidence: 99%

Coordinated torque control for enhanced steering and stability of independently driven mobile robots

Wang,

Wang

2024

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Purpose Mobile robots with independent wheel control face challenges in steering precision, motion stability and robustness across various wheel and steering system types. This paper aims to propose a coordinated torque distribution control approach that compensates for tracking deviations using the longitudinal moment generated by active steering. Design/methodology/approach Building upon a two-degree-of-freedom robot model, an adaptive robust controller is used to compute the total longitudinal moment, while the robot actuator is regulated based on the difference between autonomous steering and the longitudinal moment. An adaptive robust control scheme is developed to achieve accurate and stable generation of the desired total moment value. Furthermore, quadratic programming is used for torque allocation, optimizing maneuverability and tracking precision by considering the robot’s dynamic model, tire load rate and maximum motor torque output. Findings Comparative evaluations with autonomous steering Ackermann speed control and the average torque method validate the superior performance of the proposed control strategy, demonstrating improved tracking accuracy and robot stability under diverse driving conditions. Research limitations/implications When designing adaptive algorithms, using models with higher degrees of freedom can enhance accuracy. Furthermore, incorporating additional objective functions in moment distribution can be explored to enhance adaptability, particularly in extreme environments. Originality/value By combining this method with the path-tracking algorithm, the robot’s structural path-tracking capabilities and ability to navigate a variety of difficult terrains can be optimized and improved.

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Vehicle direct yaw moment control system based on the improved linear quadratic regulator

Cited by 7 publications

References 21 publications

Research on Yaw Stability Control Strategy for Distributed Drive Electric Trucks

Research on Yaw Stability Control Strategy for Distributed Drive Electric Trucks

Enhancing Autonomous Vehicle Stability through Pre-Emptive Braking Control for Emergency Collision Avoidance

Coordinated torque control for enhanced steering and stability of independently driven mobile robots

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