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Integrated optimal dynamics control of 4WD4WS electric ground vehicle with tire-road frictional coefficient estimation

Wang, Rongrong ; Hu, Chuan ; Wang, Zejiang ; Yan, Fengjun ; Chen, Nan

Mechanical systems and signal processing, 2015-08, Vol.60-61, p.727-741 [Periódico revisado por pares]

Elsevier Ltd

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  • Título:
    Integrated optimal dynamics control of 4WD4WS electric ground vehicle with tire-road frictional coefficient estimation
  • Autor: Wang, Rongrong ; Hu, Chuan ; Wang, Zejiang ; Yan, Fengjun ; Chen, Nan
  • Assuntos: Active steering ; Automotive components ; Brushes ; Dynamic control ; Electric vehicle ; Friction ; Grounds ; In-wheel motor ; Mechanical systems ; Optimization ; Robust control ; Tires ; Vehicle lateral dynamics ; Vehicles
  • É parte de: Mechanical systems and signal processing, 2015-08, Vol.60-61, p.727-741
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
  • Descrição: This paper presents an integrated optimal dynamics control of four-wheel driving and four-wheel steering (4WD4WS) electric ground vehicles via hierarchical control methodology. In the higher-level design, an LQR controller is proposed to obtain the integrated lateral force and yaw moment, according to their respective reference values. The lower-level controller is designed to ensure all the tires work in the stable region while realizing the tracking control of the vehicle dynamics. The tire-road friction coefficient is estimated through the integrated longitudinal force and lateral force, respectively, using a brush tire model. To reduce the estimation error, a novel data fusion function is employed to generate the final estimation value. Finally, the effectiveness of the proposed control and estimation strategies is validated via CarSim–Simulink joint simulation. •The control gain is obtained with an analytical solution of the Algebraic Riccati Equation.•A Barrier Function is used to ensure all the tires work in the stable region.•An analytic solution of allocation method is designed.•A data fusion method is proposed to minimize the TRFC estimation error.
  • Editor: Elsevier Ltd
  • Idioma: Inglês
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