Modeling, simulation and validation of 14 DOF full vehicle model

Setiawan, Joga Dharma; Safarudin, Mochamad; Singh, Amrik Singh Phuman

doi:10.1109/icici-bme.2009.5417285

Cited by 46 publications

(19 citation statements)

References 2 publications

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“…Furthermore, in the simulation, a 14 DOF model will be implemented for the testing of the proposed approach . The yaw moment M z produced by the controller is fed to the plant model with no introduction of additional dynamics.…”

Section: Vehicle Lateral Stability Applicationmentioning

confidence: 99%

“…The performance is compared with that produced by the base algorithm (O‐SOSM), the STSM one and the switched‐adaptive version of the suboptimal one (STBA‐SSOSM). The vehicle model used for the simulation is a 14 DOF representation of a midsized passenger car, implemented in MATLAB Simulink, with nonlinear Pacejka models for the generation of the tire‐road contact forces. The 14 DOF model is significantly more accurate than the one used for the design of the controller, which contributes to the validity of the assessment of the control laws considered.…”

Section: Simulationsmentioning

confidence: 99%

“…Furthermore, in the simulation, a 14 DOF model will be implemented for the testing of the proposed approach. 59 The yaw moment M z produced by the controller is fed to the plant model with no introduction of additional dynamics. In particular, it is not specified whether torque vectoring (as in eg, the work of Goggia et al 22 ), an active differential (see the work of Canale et al 60 ), or another implementation is adopted for the actuation (see the work of Regolin et al 19 for more details on these aspects).…”

Section: Vehicle Modelingmentioning

confidence: 99%

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Optimization‐based adaptive sliding mode control with application to vehicle dynamics control

Ferrara

Incremona

Regolin

2018

Intl J Robust & Nonlinear

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Summary This paper presents the design of a new adaptive optimization‐based second‐order sliding mode control algorithm for uncertain nonlinear systems. It is designed on the basis of a second‐order sliding mode control with optimal reaching, with the aim of reducing the control effort while maintaining all the positive aspects in terms of finite‐time convergence and robustness in front of matched uncertainties. These features are beneficial to guarantee good performance in case of vehicle dynamics control, a crucial topic in the light of the increasing demand of semiautonomous and autonomous driving capabilities in commercial vehicles. The new proposal is theoretically analyzed, as well as verified relying on an extensive comparative study, carried out on a realistic simulator of a 4‐wheeled vehicle, in the case of a lateral stability control system.

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Section: Vehicle Lateral Stability Applicationmentioning

confidence: 99%

Section: Simulationsmentioning

confidence: 99%

Section: Vehicle Modelingmentioning

confidence: 99%

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Optimization‐based adaptive sliding mode control with application to vehicle dynamics control

Ferrara

Incremona

Regolin

2018

Intl J Robust & Nonlinear

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“…CarSim is used for analyzing vehicle dynamics and assessing performance and it is endowed with a large database of vehicles and automotive elements. Due to its highly reliable models, CarSim is widely used as seen in numerous publications [ 32 , 33 , 34 ].…”

Section: Introductionmentioning

confidence: 99%

Reset Controller Design Based on Error Minimization for a Lane Change Maneuver

Cerdeira

Falcon

Delgado

et al. 2018

Sensors

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An intelligent vehicle must face a wide variety of situations ranging from safe and comfortable to more aggressive ones. Smooth maneuvers are adequately addressed by means of linear control, whereas more aggressive maneuvers are tackled by nonlinear techniques. Likewise, there exist intermediate scenarios where the required responses are smooth but constrained in some way (rise time, settling time, overshoot). Due to the existence of the fundamental linear limitations, which impose restrictions on the attainable time-domain and frequency-domain performance, linear systems cannot provide smoothness while operating in compliance with the previous restrictions. For this reason, this article aims to explore the effects of reset control on the alleviation of these limitations for a lane change maneuver under a set of demanding design conditions to guarantee a suitable ride quality and a swift response. To this end, several reset strategies are considered, determining the best reset condition to apply as well as the magnitude thereto. Concerning the reset condition that triggers the reset action, three strategies are considered: zero crossing of the controller input, fixed reset band and variable reset band. As far as the magnitude of the reset action is concerned, a full-reset technique is compared to a Lyapunov-based error minimization method to calculate the optimal reset percentage. The base linear controller subject to the reset action is searched via genetic algorithms. The proposed controllers are validated by means of CarSim.

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“…2,3 In this model, polynomial functions were used which were obtained from the suspension analysis simulations in Adams/ Car. 4 One advantage of this approach is that the suspension curves (such as the relationship between the camber and the vertical displacement) can be evaluated simply by adjusting these curves directly in the model, rather than by modifying specific hard-point locations.…”

Section: Introductionmentioning

confidence: 99%

Improvement in the dynamic responses of the semiempirical vehicle model using the Maxwell force model for the suspension forces

Yoo

2015

Proceedings of the Institution of Mechanical Engineers, Part D:

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A multi-body vehicle model is more accurate than a lumped-mass model; however, it usually requires a cumbersome process to obtain accurate data for compliant elements such as bushings. To avoid this complex process associated with bushings or kinematic linkages, a semiempirical vehicle model was developed by switching the suspension characteristics to measured kinematic and compliance data. In a semiempirical vehicle model, it is assumed that the inertia effects in the suspension subsystem are ignored by replacing components of the suspension system with massless links. Although this semiempirical vehicle model is simple, it sometimes degrades the accuracy in the dynamic responses compared with those of the multi-body vehicle model. Thus, it is necessary to include the dynamic characteristics of the suspension in an semiempirical vehicle model to increase its accuracy. In this paper, a new technique is proposed that considers the dynamic effect of the suspension system in an semiempirical vehicle model by using the Maxwell force model. By comparing the vertical acceleration with that obtained by the proposed method, the dynamic responses with the semiempirical vehicle model become much closer to those of the multi-body vehicle model. The results show that the force with respect to the suspension's motion has a strong effect on the dynamic responses, and this relationship becomes important to improve the accuracy of the semiempirical vehicle model.

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Modeling, simulation and validation of 14 DOF full vehicle model

Cited by 46 publications

References 2 publications

Optimization‐based adaptive sliding mode control with application to vehicle dynamics control

Optimization‐based adaptive sliding mode control with application to vehicle dynamics control

Reset Controller Design Based on Error Minimization for a Lane Change Maneuver

Improvement in the dynamic responses of the semiempirical vehicle model using the Maxwell force model for the suspension forces

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