Dual back-stepping observer to anticipate the rollover risk in under/over-steering situations. Application to ATVs in off-road context
| Autor: | Roland Lenain, Mathieu Richier, Christophe Debain, Benoit Thuilot |
|---|---|
| Přispěvatelé: | Technologies et systèmes d'information pour les agrosystèmes (UR TSCF), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Laboratoire des sciences et matériaux pour l'électronique et d'automatique (LASMEA), Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2012 |
| Předmět: |
0209 industrial biotechnology
Engineering Observer (quantum physics) Extrapolation Stability (learning theory) Context (language use) 02 engineering and technology PREVENTION DES RISQUES 020901 industrial engineering & automation Control theory WHEELED ROBOTS 0502 economics and business ADAPTATIVE CONTROL Simulation 050210 logistics & transportation Supervisor business.industry 05 social sciences Rollover FIELD ROBOTS Model predictive control [SDE]Environmental Sciences ROBOT Robot RENVERSEMENT DE VEHICULE business |
| Zdroj: | IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS'12 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS'12, Oct 2012, Vilamoura, Portugal. 6 p IROS |
| Popis: | [Departement_IRSTEA]Ecotechnologies [TR1_IRSTEA]INSPIRE; International audience; In this paper an ATV (All-Terrain Vehicle) rollover prevention system is proposed. It is based on the online estimation and prediction of the Lateral Load Transfer (LLT), allowing the evaluation of dynamic instabilities. Using a vehicle model based on two 2D representations, the LLT can be estimated and predicted. As we consider off road vehicle, grip conditions must be encountered and are here estimated thanks to observation theory. Nevertheless, two main behaviours (over/under-steering) may be encountered pending on grip, and vehicle configuration. Because of the low cost sensor, these two opposite dynamics cannot be explicitly discriminated. As a result, two observers are used according to the vehicle behaviour. Based on a bicycle model and a low cost perception system, they estimate on-line the terrain properties (grip conditions, global sideslip angle and bank angle). A "supervisor" selects on-line the right observer. Associated to a predictive control algorithm, based on the extrapolation of rider's action and the selected estimated dynamical state, the risk can be anticipated, enabling to warn the pilot and to consider the implementation of active actions. Simulations and full-scale experimentations are presented to discuss about the efficiency of the proposed solution. |
| Databáze: | OpenAIRE |
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