Improving yaw dynamics by feedforward rear wheel steering

Besselink, Igo Igo; Veldhuizen, Tjalling; Nijmeijer, Henk

doi:10.1109/ivs.2008.4621314

Cited by 7 publications

(6 citation statements)

References 2 publications

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“…Cui et al (2008) designed a robust controller for ARS, aiming at eliminating the bad influence from external disturbance and model uncertainty on the tracking performance of yaw rate. Two ARS control algorithms including yaw rate feedback as well as feedforward were discussed (Besselink et al, 2008) and the latter only required the front wheel steering angle and vehicle forward velocity. An integrated chassis control framework for a novel three-axle electric bus with ARS was described, where there were four controllers (Liu et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Stability control of in-wheel motor electric vehicles under extreme conditions

Zhao

Wang

Zhang

2018

Transactions of the Institute of Measurement and Control

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To investigate the stability of in-wheel motor electric vehicles (IWMEVs) under extreme conditions, a novel control strategy including active rear steering (ARS) mode and direct yaw moment control (DYC) mode is proposed in this paper, utilizing the adaptive dynamic neural network (ADNN) algorithm to make the most of the two control modes. Firstly, a three-degree of freedom nonlinear vehicle model as well as some subsystems is established. Then, a two-layer stability control strategy is put forward, where the upper-layer calculates the desired rear steering angle as well as the differential torque of the rear wheels and the lower-layer executes commands and returns relevant signals. Besides, a stability controller based on ADNN algorithm is designed in the upper-layer so as to take advantage of the two modes under extreme conditions. Next, the impacts of initial values of the connection weights on the ability of ADNN algorithm to train and learn are revealed. Consequently, the optimal initial values can be ascertained before the following simulations. Finally, the closed loop simulations of ARS and DYC are carried out under some extreme conditions such as high velocity and low adhesion coefficient roads, and the results indicate that compared with DYC’s difficulty in playing its role, ARS mode can significantly improve the stability of IWMEVs even under extreme conditions.

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Section: Introductionmentioning

confidence: 99%

Stability control of in-wheel motor electric vehicles under extreme conditions

Zhao

Wang

Zhang

2018

Transactions of the Institute of Measurement and Control

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“…Nowadays, we can find two types of assistance systems: systems in an open-loop control strategy only, and systems in a closed one. Assistance systems as the 4WS system are actually implemented by car manufacturers in an open-loop using feedforward controllers [7], [8]. Even though, several efforts have been deployed to close the loop [9]- [11], to the best of our knowledge, few technological issues, particularly regarding communication delays and sensors noises, still inhibit car manufacturers to adopt a closed loop architecture.…”

Section: Introductionmentioning

confidence: 99%

Optimal Yaw Rate Control for Over-Actuated Vehicles

Kissai¹,

Monsuez²,

Mouton³

et al. 2020

SAE Technical Paper Series

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As we are heading towards autonomous vehicles, additional driver assistance systems are being added. The vehicle motion is automated step by step to ensure passengers' safety and comfort, while still preserving vehicle performance. However, simultaneous activations of concurrent systems may conflict, and non-suitable behavior may emerge. Our research work consists in proving that with the right coordination approach, simultaneous operation of different systems improve the vehicle's performance and avoid the emergence of unwanted conflicts. To prove this, we gathered different control architectures implemented in commercial passenger cars, and we compared them with our control architecture using a unified reference vehicle model. The high-fidelity vehicle model is developed in Simcenter Amesim in a modular and extensible manner. This enables adding systems in a plug-and-play way. Not only different control architectures can be tested on the same vehicle, but also different systems combinations can be evaluated. In this research, the vehicle can steer at the front and at the rear, and each wheel can be braked independently. Each of the actuators concerned can influence the vehicle's yaw rate leading in some cases in system conflicts. More complex control strategies are then implemented in Matlab/Simulink, and co-simulations are carried between both softwares in order to provide realistic results. It has been shown that optimal control allocation algorithms are more suitable to coordinate systems in an over-actuated vehicle. Moreover, if the optimization objectives are well formalized, performance, safety and comfort can be improved since the vehicle can benefit from the systems' synergies.

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“…Lateral dynamics controls systems typically consist of a yaw rate reference follower or sideslip angle limiter; a feedback controller generates as outputs the required yaw moment or steer angle. Early papers on active rear steering have focused on reducing the vehicle sideslip angle, whereas more recent ones concentrate their attention on the yaw motion control [19] [16]. Canale et al reported that it is necessary to control the sideslip angle along with the yaw motion to maintain the stability of a vehicle.…”

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confidence: 99%

A cooperative control strategy for yaw rate and sideslip angle control combining torque vectoring with rear wheel steering

Vignati

Sabbioni

2021

Vehicle System Dynamics

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Automobiles are becoming more and more complex as multiple control systems are integrated into the vehicle platform. This paper investigates the coordination of active rear steering (RWS) and torque vectoring (TV) -which is enabled by independent electric motors at the rear axle -in controlling vehicle lateral dynamics. Specifically, the proposed controller aims at enhancing vehicle handling performance and stability while cornering. The coordination of the two control systems is achieved by weighting their contribution based on their impact on vehicle dynamics according to the working condition. The impact of each control system is assessed by means of phase portraits. These plots are in fact a very powerful tool for analyzing vehicle nonlinear dynamics as they readily display vehicle stability properties and map equilibrium point locations and movement to changing parameters and control inputs. Based on phase portrait analysis, a performance index is thus proposed, which weights more the control action (TV or RWS) capable of leading the vehicle at the nearest equilibrium point with the fastest rate. The controller performance is assessed through numerical simulations carried out using a nonlinear 14 dofs vehicle model. Results are compared with ones of the two controllers alone (RWS and TV) in different cornering maneuvers and considering different adherence conditions.

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Improving yaw dynamics by feedforward rear wheel steering

Cited by 7 publications

References 2 publications

Stability control of in-wheel motor electric vehicles under extreme conditions

Stability control of in-wheel motor electric vehicles under extreme conditions

Optimal Yaw Rate Control for Over-Actuated Vehicles

A cooperative control strategy for yaw rate and sideslip angle control combining torque vectoring with rear wheel steering

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