A combined use of phase plane and handling diagram method to study the influence of tyre and vehicle characteristics on stability

Farroni, Flavio; Russo, Michele; Russo, Riccardo; Terzo, Mario; Timpone, Francesco

doi:10.1080/00423114.2013.797590

Cited by 61 publications

(33 citation statements)

References 13 publications

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“…The TV yaw moment is directly applied as an input to the yaw moment balance equation. Several combinations of β andβ are imposed as initial conditions for the model, which is run for a high tire-road friction coefficient with constant steering wheel angle (50 • ) and vehicle speed (80 km/h) [25], [52]. This process identifies the set of initial conditions providing stable response.…”

Section: A Phase-plane Analysismentioning

confidence: 99%

Yaw Rate and Sideslip Angle Control Through Single Input Single Output Direct Yaw Moment Control

Lenzo

Zanchetta

Sorniotti

et al. 2021

IEEE Trans. Contr. Syst. Technol.

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Electric vehicles with independently controlled drivetrains allow torque vectoring, which enhances active safety and handling qualities. This article proposes an approach for the concurrent control of yaw rate and sideslip angle based on a single-input single-output (SISO) yaw rate controller. With the SISO formulation, the reference yaw rate is first defined according to the vehicle handling requirements and is then corrected based on the actual sideslip angle. The sideslip angle contribution guarantees a prompt corrective action in critical situations such as incipient vehicle oversteer during limit cornering in low tire-road friction conditions. A design methodology in the frequency domain is discussed, including stability analysis based on the theory of switched linear systems. The performance of the control structure is assessed via: 1) phase-plane plots obtained with a nonlinear vehicle model; 2) simulations with an experimentally validated model, including multiple feedback control structures; and 3) experimental tests on an electric vehicle demonstrator along step steer maneuvers with purposely induced and controlled vehicle drift. Results show that the SISO controller allows constraining the sideslip angle within the predetermined thresholds and yields tire-road friction adaptation with all the considered feedback controllers. Index Terms-Controlled drift, electric vehicle, experimental tests, sideslip angle control, tire-road friction coefficient, torque vectoring (TV), yaw rate control. NOMENCLATURE a Front semiwheelbase. a x Longitudinal acceleration. a y Lateral acceleration. A, B, C State-space matrices of the plant. A i , B i , C i , D i State-space matrices of the plants considered in the stability analysis (i = 1, 2, 3). A s , B s , C s State-space matrices of the shaped plant.

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Section: A Phase-plane Analysismentioning

confidence: 99%

Yaw Rate and Sideslip Angle Control Through Single Input Single Output Direct Yaw Moment Control

Lenzo

Zanchetta

Sorniotti

et al. 2021

IEEE Trans. Contr. Syst. Technol.

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“…It is necessary to consider that system (1) should not be used for transitory analysis and the achieved results allow only qualitative considerations. In the three figures the blue lines refer to very stable conditions: in the time histories of the seven states and in the 3D plot of the car states, no oscillating behaviors can be seen (the equilibrium point appears to be a "node") [17], [18].…”

Section: Theoretical Resultsmentioning

confidence: 99%

Precursors of Instability for a Vehicle Traveling in Curve

Massa

Pastore

Russo

et al. 2019

IREMOS

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Nowadays, the automotive field takes care of human safety in the everyday driving. In this sense, the vehicle dynamics control systems and ADAS (Advanced Driver Assistance System) play a crucial role for both active and passive safety. In terms of handling, the critical issues for the everyday driver are during braking or steering maneuvers: the main idea of this paper is to investigate the possibility to recognize some critical behaviors leading to vehicle instability, using only the wheel sensors signals, already available as mandatory in actual passenger car (in particular for ABS). In order to do this, a seven degrees of freedom model of a vehicle that runs in a curve with constant longitudinal speed is formulated. With no particular reference to the power unit (IC motor or electrical motors), three different traction schemes configurations are considered. The numerical solutions of the model are analyzed to evaluate their stability, the associated modal shapes and the modal participation factors. A systematic analysis allows recognizing some vehicle behaviors that may constitute instability precursors useful to develop a suitable control logic. Results provided by both a standard vehicle dynamics simulation software and a "on-road" test are presented and discussed to verify the actual feasibility of the proposed precursors employment.

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“…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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A combined use of phase plane and handling diagram method to study the influence of tyre and vehicle characteristics on stability

Cited by 61 publications

References 13 publications

Yaw Rate and Sideslip Angle Control Through Single Input Single Output Direct Yaw Moment Control

Yaw Rate and Sideslip Angle Control Through Single Input Single Output Direct Yaw Moment Control

Precursors of Instability for a Vehicle Traveling in Curve

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

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