Reconfigurable control structure to prevent the rollover of heavy vehicles

Gáspár, Péter; Szászi, István; Bokor, József

doi:10.1016/j.conengprac.2004.06.003

Cited by 63 publications

(42 citation statements)

References 16 publications

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“…The lateral dynamics, however, cannot be abstracted away to the same extent using controllers, especially when constraints such as the danger of roll-over must be considered in extreme maneuvers [29], [30]. For this reason, our models consider increasingly complex lateral vehicle dynamics and tire models: point-mass model, kinematic single-track model, single-track model, and a multi-body model.…”

Section: Vehicle Modelsmentioning

confidence: 99%

CommonRoad: Composable benchmarks for motion planning on roads

Althoff

Koschi

Manzinger

2017

2017 IEEE Intelligent Vehicles Symposium (IV)

247

166

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Abstract-Numerical experiments for motion planning of road vehicles require numerous components: vehicle dynamics, a road network, static obstacles, dynamic obstacles and their movement over time, goal regions, a cost function, etc. Providing a description of the numerical experiment precise enough to reproduce it might require several pages of information. Thus, only key aspects are typically described in scientific publications, making it impossible to reproduce results-yet, reproducibility is an important asset of good science. Composable benchmarks for motion planning on roads (CommonRoad) are proposed so that numerical experiments are fully defined by a unique ID; all information required to reconstruct the experiment can be found on the CommonRoad website. Each benchmark is composed by a vehicle model, a cost function, and a scenario (including goals and constraints). The scenarios are partly recorded from real traffic and partly hand-crafted to create dangerous situations. We hope that CommonRoad saves researchers time since one does not have to search for realistic parameters of vehicle dynamics or realistic traffic situations, yet provides the freedom to compose a benchmark that fits one's needs.

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Section: Vehicle Modelsmentioning

confidence: 99%

CommonRoad: Composable benchmarks for motion planning on roads

Althoff

Koschi

Manzinger

2017

2017 IEEE Intelligent Vehicles Symposium (IV)

247

166

View full text Add to dashboard Cite

show abstract

“…Gaspar developed a reconfigurable control system for heavy vehicles, it combined active anti-roll bar control and active braking control [9]. At the same time, the optimal anti-rollover control strategy considered several roll dynamics parameters, such as roll angle, roll angular velocity, lateral angular velocity and lateral acceleration.…”

Section: Introductionmentioning

confidence: 99%

Anti-rollover Control System Design of Articulated Off-road Vehicles

Wang

Zhang

Chen

et al. 2016

MATEC Web of Conferences

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Abstract. This paper designs an anti-rollover control system to improve articulated off-road vehicle rollover safety. This controller consists two level control which target to two different instability stages: 1) the active swing bridge adjustment control algorithm for the first stage instability; and 2) the active braking control algorithm for the second stage instability. The first level control is based on fuzzy PID control and second is based on switch control, they work together to prevent the articulated wheel loader from rollover accident. Multi-body dynamics model of a ZL50 articulated wheel loader is built by commercial software RecurDyn, and the MBD model and controller model were co-simulated under the same solver in RecurDyn/CoLink. The co-simulation results show that, during the high speed steering rollover process, these two level controller work efficiently to reduce the roll angle and improve the vehicle rollover stability.

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“…Several control design problems for active anti-roll bar systems have been investigated with many different approaches during the last decades. In (Gaspar et al, 2005a) the authors present Linear Parameter Varying (LPV) techniques to control the active anti-roll bars, combined with an active brake control on single unit heavy vehicles. The forward velocity is considered as the varying parameter.…”

Section: Introductionmentioning

confidence: 99%

Multi objective H∞ active anti-roll bar control for heavy vehicles

et al. 2017

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In the active anti-roll bar control on heavy vehicles, roll stability and energy consumption of actuators are two essential but conflicting performance objectives. In a previous work, the authors proposed an integrated model, including four electronic servo-valve hydraulic actuators in a linear yawroll model on a single unit heavy vehicle. This paper aims to design an active anti-roll bar control and solves a Multi-Criteria Optimization (MCO) problem formulated as an H ∞ control problem where the weighting functions are optimally selected through the use of Genetic Algorithms (GAs). Thanks to GAs, the roll stability and the energy consumption are handled using a single high level parameter and illustrated via the Pareto optimality. Simulation results emphasize the simplicity and efficiency of the use of the GAs method for a MCO problem in H ∞ active anti-roll bar control on heavy vehicles.

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Reconfigurable control structure to prevent the rollover of heavy vehicles

Cited by 63 publications

References 16 publications

CommonRoad: Composable benchmarks for motion planning on roads

CommonRoad: Composable benchmarks for motion planning on roads

Anti-rollover Control System Design of Articulated Off-road Vehicles

Multi objective H∞ active anti-roll bar control for heavy vehicles

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