A model-based rear-end collision avoidance algorithm for heavy commercial road vehicles

Rajaram, Vignesh; Subramanian, Shankar C.

doi:10.1177/0954407014547243

Cited by 3 publications

(4 citation statements)

References 21 publications

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“…An expression for the brake force produced by an S-cam brake was given by Limbert. 14 Rajaram et al 15 modified this equation as…”

Section: Influence Of Pushrod Stroke On Vehicle Braking Dynamicsmentioning

confidence: 99%

“…Different road surface friction coefficients, with m = 0.8 (dry surface) and m = 0.35 (snowy surface). 15 3. Split m road surface condition: In this study, drysnow split m condition was considered.…”

Section: Influence Of Pushrod Stroke On Vehicle Braking Dynamicsmentioning

confidence: 99%

“…Different road surface friction coefficients, with µ = 0.8 (dry surface) and µ = 0.35 (snowy surface). 15…”

Section: Influence Of Pushrod Stroke On Vehicle Braking Dynamicsmentioning

confidence: 99%

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Artificial neural network approach for air brake pushrod stroke prediction in heavy commercial road vehicles

Raveendran

Suresh

Rajaram

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part D:

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In heavy commercial road vehicles, the air brake system is a critical vehicle safety system whose performance degradation increases the risk of accidents and hence requires periodic inspection and maintenance. The wear of brake pad lining and brake drum during operation leads to increase in the stroke of a component called pushrod whose ‘out-of-adjustment’ creates severe brake performance degradation. The fact that the driver does not receive a corresponding tactile feedback till it is too severe adds to the complexity of manual detection. Motivated by the increase in onboard sensing, electronics, and computation capabilities, this study proposes an artificial neural network–based approach to predict pushrod stroke based on measurement of brake chamber pressure. Here, a back propagation algorithm was used to train the multilayer feed-forward network. The effect of excessive pushrod stroke on vehicle braking response was first studied using a Hardware-in-Loop system that consists of brake system hardware and a commercial vehicle dynamics simulation software (IPG TruckMaker®). Experimental data collected from this system with manual slack adjuster and automatic slack adjuster have then been used to train and test the artificial neural network for pushrod stroke prediction. The performance of the prediction scheme has been tested over the entire range of brake operating conditions. The prediction error corresponding to manual slack adjuster was found to be within ±15% in 322 out of the entire test set of 328 instances (98.17%) and automatic slack adjuster within ±8% in all 57 test sets (100%). Statistical analysis based on confidence interval revealed a prediction error between −1.62% and −3.05% for manual slack adjuster and 0.43% and −1.62% for automatic slack adjuster for 99% confidence interval, which demonstrated the efficacy of the proposed prediction scheme.

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“…An expression for the brake force produced by an S-cam brake was given by Limbert. 14 Rajaram et al 15 modified this equation as…”

Section: Influence Of Pushrod Stroke On Vehicle Braking Dynamicsmentioning

confidence: 99%

Section: Influence Of Pushrod Stroke On Vehicle Braking Dynamicsmentioning

confidence: 99%

See 1 more Smart Citation

Artificial neural network approach for air brake pushrod stroke prediction in heavy commercial road vehicles

Raveendran

Suresh

Rajaram

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…There have been some investigations 8–11 on improving the lateral stability of articulated vehicles in which control systems are used. Sampson 12 proposed an active roll control system using a linear quadratic regulator (LQR) controller to prevent vehicle rollover.…”

Section: Introductionmentioning

confidence: 99%

Improvement in the rollover stability of a liquid-carrying articulated vehicle via a new robust controller

Saeedi

Kazemi

Azadi

2016

Proceedings of the Institution of Mechanical Engineers, Part D:

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In this paper, in order to improve the roll stability of an articulated vehicle carrying a liquid, an active roll control system is utilized by employing two different control methods. First, a 16-degree-of-freedom non-linear dynamic model of an articulated vehicle is developed. Next, the dynamic interaction of the liquid cargo with the vehicle is investigated by integrating a quasi-dynamic liquid sloshing model with a tractor-semitrailer model. Initially, to improve the lateral dynamic stability of the vehicle, an active roll control system is developed using classical integral sliding-mode control. The active anti-roll bar is employed as an actuator to generate the roll moment. Next, in order to verify the classical sliding-mode control performance and to eliminate its chattering, the backstepping method and the sliding-mode control method are combined. Subsequently, backstepping sliding-mode control as a new robust control is implemented. Moreover, in order to prevent both yaw instability and jackknifing, an active steering control system is designed on the basis of a simplified three-degree-of-freedom dynamic model of an articulated vehicle carrying a liquid. In the introduced system, the yaw rate of the tractor, the lateral velocity of the tractor and the articulation angle are considered as the three state variables which are targeted in order to track their desired values. The simulation results show that the combined proposed roll control system is more successful in achieving target control and reducing the lateral load transfer ratio than is classical sliding-mode control. A more detailed investigation confirms that the designed active steering system improves both the lateral stability of the vehicle and its handling, in particular during a severe lane-change manoeuvre in which considerable instability occurs.

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Design and hardware-in-loop implementation of collision avoidance algorithms for heavy commercial road vehicles

Rajaram

Subramanian

2016

Vehicle System Dynamics

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A model-based rear-end collision avoidance algorithm for heavy commercial road vehicles

Cited by 3 publications

References 21 publications

Artificial neural network approach for air brake pushrod stroke prediction in heavy commercial road vehicles

Artificial neural network approach for air brake pushrod stroke prediction in heavy commercial road vehicles

Improvement in the rollover stability of a liquid-carrying articulated vehicle via a new robust controller

Design and hardware-in-loop implementation of collision avoidance algorithms for heavy commercial road vehicles

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