The relationship between vehicle yaw acceleration response and steering velocity for steering control

Thoresson, Michael John; Botha, Theunis R.; Schalk, P.

doi:10.1504/ijvd.2014.058487

Cited by 5 publications

(4 citation statements)

References 19 publications

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“…the steer angle of the previous time‐step. The resultant transfer function for the model is given in (2), where the unmodelled residuals have been dropped, the numerator represents the exogenous inputs, and the denominator the previous samples

H (), ((, z)) = \frac{η_{0} z^{- n}}{1 + φ_{0} z^{- 1}}

Re‐writing (2) in the difference form results in (3), where

\overset{false~}{y} ()(, k)

represents the estimated yaw rate of the vehicle at the current time‐step,

y ()(, k - 1)

represents the measured yaw rate of the vehicle at the previous time‐step,

u ()(, k - n) \underset{}{\to} u ()(, k - 1)

represents the steering angle of the vehicle at the previous time‐step ( n = 1 as the input dependency's time‐step delay), while

φ_{0}

and

η_{0}

are the weighting parameters on the previous time‐step's yaw rate and previous time‐step's steer angle, respectively

\overset{false~}{y} ()(, k) = φ_{0} y ()(, k - 1) + η_{0} u ()(, k - n)

To solve for the weighting variables of the ARX‐model, the principle of linear least squares estimation is used, as presented in [26]. This method requires a set of sample points that are used to estimate the weighting variables of (3).…”

Section: Steering Controller Design: Vehicle Modelmentioning

confidence: 99%

Influence of wireless communication transport latencies and dropped packages on vehicle stability with an offsite steering controller

Bennett

Kapp

Botha

et al. 2020

IET Intelligent Transport Systems

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H (), ((, z)) = \frac{η_{0} z^{- n}}{1 + φ_{0} z^{- 1}}

Re‐writing (2) in the difference form results in (3), where

\overset{false~}{y} ()(, k)

represents the estimated yaw rate of the vehicle at the current time‐step,

y ()(, k - 1)

represents the measured yaw rate of the vehicle at the previous time‐step,

u ()(, k - n) \underset{}{\to} u ()(, k - 1)

represents the steering angle of the vehicle at the previous time‐step ( n = 1 as the input dependency's time‐step delay), while

φ_{0}

and

η_{0}

are the weighting parameters on the previous time‐step's yaw rate and previous time‐step's steer angle, respectively

\overset{false~}{y} ()(, k) = φ_{0} y ()(, k - 1) + η_{0} u ()(, k - n)

Section: Steering Controller Design: Vehicle Modelmentioning

confidence: 99%

Influence of wireless communication transport latencies and dropped packages on vehicle stability with an offsite steering controller

Bennett

Kapp

Botha

et al. 2020

IET Intelligent Transport Systems

View full text Add to dashboard Cite

“…Directional control is often studied with the aim of developing a driver steering model to be used in conjunction with vehicle simulation models. Thoresson et al (2014) and Kapp and Els (2015) controlled two vehicle states to develop such a driver model, namely the desired yaw acceleration and the lateral offset from the path. The desired yaw acceleration was related to the desired steering wheel input velocity (which is directly proportional to the yaw rate) and was used as the main path following controller.…”

Section: Existing Directional Stability Assessment Criteriamentioning

confidence: 99%

ABS performance evaluation taking braking, stability and steerability into account

Hamersma

Schalk

2017

IJVSMT

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Extensive research on the design and development of antilock brake systems has been done in the past. ABS has been developed not to reduce stopping distance, but rather to stop as quickly as possible whilst maintaining some directional stability and steering control during hard braking. There is however not a single, quantifiable, scientific method in which the performance of these ABS control systems can be compared with one another that takes all the important aspects into account. This investigation proposes an evaluation technique that considers the ABS control system's exploitation of the entire friction circle. Five scenarios are investigated and the performance of two ABS algorithms are compared with one another and with a conventional brake system without ABS. The resulting technique is a clear and concise, multi-parameter comparative tool that can be used to critically compare the performance of various brake systems under the same testing conditions.

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“…By substituting equation (10) in equation (6) and, finally, placing it in equation (4) and then using Laplace operator, equations (4) and (11) are changed into state-space model. Thus, the following general equation will be achieved…”

Section: Vehicle Dynamicmentioning

confidence: 99%

“…Vehicle velocity depends on the road conditions such as the friction coefficient of road surface, which is different for various roads. 11 State predictor-based model reference adaptive control (PMRAC) is capable of resisting against these uncertainties and external disturbances. Comparing to a state observer, state predictor works based on the current state of the system and any other external signals.…”

Section: Introductionmentioning

confidence: 99%

Predictor-based model reference adaptive control for a vehicle lateral dynamics considering uncertainties

Khosravi

Lachini

Sarhadi

2015

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article deals with the design and analysis of state predictor-based model reference adaptive control for a vehicle lateral dynamics. The goal of this article is to achieve the desired tracking in the presence of uncertainty in order to guarantee the stability as well as improving the performance of the vehicle. Through Lyapunov stability analysis, the adaptive laws of stable predictor-based state feedback have been derived. In order to evaluate the advantages of proposed controller, first the performance of non-adaptive linear quadratic regulator controller in nominal conditions has been presented. By adding uncertainty to the vehicle dynamics, the performance of controller has been reduced. Then, by augmenting model reference adaptive control to linear quadratic regulator, the performance has been improved. Finally, a considerable improvement has been achieved in transient response and control signal using predictor-based model reference adaptive control. Simulation results show that by adding predictor-based model reference adaptive control to the system, uncertain part of the vehicle dynamics is approximated, and the tracking structure of integrated control (linear quadratic regulator + predictor-based model reference adaptive control) has been successful. KeywordsPredictor-based model reference adaptive control, vehicle lateral dynamic, uncertainty, linear quadratic regulator controller, tracking error Date

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The relationship between vehicle yaw acceleration response and steering velocity for steering control

Cited by 5 publications

References 19 publications

Influence of wireless communication transport latencies and dropped packages on vehicle stability with an offsite steering controller

Influence of wireless communication transport latencies and dropped packages on vehicle stability with an offsite steering controller

ABS performance evaluation taking braking, stability and steerability into account

Predictor-based model reference adaptive control for a vehicle lateral dynamics considering uncertainties

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