Nonlinear observer design for automatic steering of vehicles

Zhang, J.R.; Xu, Sun

doi:10.1109/itsc.2002.1041210

Cited by 7 publications

(5 citation statements)

References 7 publications

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“…Reference [19] gives a multirate lateral velocity observer to comply with lateral position measurements from magnetic markers that are evenly spaced. For more examples, see [23] and references therein. Because of the highly nonlinear and coupled nature of vehicle dynamics, these works have no convergence proof, have used simplified models, and/or have been developed independent of the control scheme.…”

Section: Nomenclature Mmentioning

confidence: 99%

Neuroadaptive Output Tracking of Fully Autonomous Road Vehicles With an Observer

Kumarawadu

Lee

2009

IEEE Trans. Intell. Transport. Syst.

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Automated vehicle control systems are a key technology for intelligent vehicle highway systems (IVHSs). This paper presents an automated vehicle control algorithm for combined longitudinal and lateral motion control of highway vehicles, with special emphasis on front-wheel-steered four-wheel road vehicles. The controller is synthesized using an online neural-estimator-based control law that works in combination with a lateral velocity observer. The online adaptive neural-estimator-based design approach enables the controller to counteract for inherent model discrepancies, strong nonlinearities, and coupling effects. The neurocontrol approach can guarantee the uniform ultimate bounds (UUBs) of the tracking and observer errors and the bounds of the neural weights. The key design features are 1) inherent coupling effects will be taken into account as a result of combining of the two control issues, viz., lateral and longitudinal control; 2) rather ad hoc numerical approximations of lateral velocity will be avoided via a combined controller-observer design; and 3) closed-loop stability issues of the overall system will be established. The algorithm is validated via a formative mathematical analysis based on a Lyapunov approach and numerical simulations in the presence of parametric uncertainties, as well as severe and adverse driving conditions. Index Terms-Automated highway vehicle, combined lateral and longitudinal control, intelligent vehicle highway system (IVHS), lateral velocity observer, neural networks (NNs), vehicle dynamics. NOMENCLATURE mVehicle mass. I z Vehicle yaw moment of inertia. fRolling friction coefficient. a Distance from the front wheel (FW) to the center of gravity (c.o.g.). bDistance from the rear wheel to the c.o.g.

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Section: Nomenclature Mmentioning

confidence: 99%

Neuroadaptive Output Tracking of Fully Autonomous Road Vehicles With an Observer

Kumarawadu

Lee

2009

IEEE Trans. Intell. Transport. Syst.

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“…Different models related to automatic steering of vehicle have been studied in the literature (see for example Zhang and Xu [2002], El Hajjaji and Bentalba [2003], Moriwaki [2005], , ). Here, we have chosen to consider the coupling model of longitudinal and lateral motions of a vehicle.…”

Section: Representation Of the Vehicle Model By A T-s Fuzzy Modelmentioning

confidence: 99%

“…Here, we have chosen to consider the coupling model of longitudinal and lateral motions of a vehicle. This model, already used in Zhang and Xu [2002], is strongly nonlinear and is given by the following equations:u…”

Section: Representation Of the Vehicle Model By A T-s Fuzzy Modelmentioning

confidence: 99%

Design of observers for Takagi-Sugeno fuzzy models for Fault Detection and Isolation

Akhenak¹,

Chadli²,

Ragot³

et al. 2009

IFAC Proceedings Volumes

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This paper addresses the design of an unknown input fuzzy observer for Takagi-Sugeno (T-S) fuzzy model subject to unknown inputs. The main contribution of the paper is the development of a robust fuzzy observer in presence of disturbances. Based on Lyapunov function, it is shown how to determine observers gains in linear matrix inequalities (LMI). The proposed T-S observer is used for detection and isolation of faults which can affect a T-S model. The proposed methodology is illustrated by estimating the yaw rate and the fault of automatic steering vehicle.

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“…and it suffices to replace j indices by i in conditions (7). [15], is strongly nonlinear and is given by the following equations:…”

Section: Sliding Mode Fuzzy Observermentioning

confidence: 99%

Fault detection and isolation using sliding mode observer for uncertain Takagi-Sugeno fuzzy model

Akhenak

Chadli²,

Ragot

et al. 2008

2008 16th Mediterranean Conference on Control and Automation

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International audienceThis paper addresses fault detection and isolation (FDI) problem using a sliding mode fuzzy observer on the basis of a uncertain Takagi-Sugeno (T-S) fuzzy model. First, a robust fuzzy observer with respect to the uncertainties is designed. The convergence of the fuzzy observer is performed by the search of suitable Lyapunov matrices. It is shown how to synthesis observers using a set of linear matrix inequalities (LMI) conditions. Once the fuzzy observer is designed, FDI problem for nonlinear systems described by T-S fuzzy systems using the fuzzy observer is presented. A bank of fuzzy observer is then designed in order to investigate fault diagnosis problems. The validity of the proposed methodology is illustrated on a dynamic vehicle model

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Nonlinear observer design for automatic steering of vehicles

Cited by 7 publications

References 7 publications

Neuroadaptive Output Tracking of Fully Autonomous Road Vehicles With an Observer

Neuroadaptive Output Tracking of Fully Autonomous Road Vehicles With an Observer

Design of observers for Takagi-Sugeno fuzzy models for Fault Detection and Isolation

Fault detection and isolation using sliding mode observer for uncertain Takagi-Sugeno fuzzy model

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