Vehicle online parameter estimation using a nonlinear adaptive observer

Boufadene, Mourad; Rabhi, Abdelhamid; Belkheiri, Mohammed; El-Hajjaji, Ahmed

doi:10.1109/acc.2016.7525046

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…Adaptive observers were introduced to study the convergence of state estimation jointly with system parameters identification [14,15]. The parameter estimation of nonlinear vehicle dynamics was investigated using a fuzzy unknown input observer (UIO) [16] and with a nonlinear adaptive observer [17].…”

Section: Related Work On Virtual Sensorsmentioning

confidence: 99%

“…Theorem 1. In view of the two-stage longitudinal and lateral subsystems subject to unknown inputs, if the polytopic interlinked models (8) satisfy the stated Assumptions 1, an NI-UIO observer is designed by (17), and the ISS convergence of the estimation errors is ensured, then the origin of the system will be practically finite-time stable, i.e., the system states will converge to the neighborhood of the origin in finite time.…”

Section: Ni-uio Stability and Convergence Analysismentioning

confidence: 99%

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Virtual Sensor: Simultaneous State and Input Estimation for Nonlinear Interconnected Ground Vehicle System Dynamics

Sentouh

Fouka

Popieul

2023

Sensors

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This paper proposes a new observer approach used to simultaneously estimate both vehicle lateral and longitudinal nonlinear dynamics, as well as their unknown inputs. Based on cascade observers, this robust virtual sensor is able to more precisely estimate not only the vehicle state but also human driver external inputs and road attributes, including acceleration and brake pedal forces, steering torque, and road curvature. To overcome the observability and the interconnection issues related to the vehicle dynamics coupling characteristics, tire effort nonlinearities, and the tire–ground contact behavior during braking and acceleration, the linear-parameter-varying (LPV) interconnected unknown inputs observer (UIO) framework was used. This interconnection scheme of the proposed observer allows us to reduce the level of numerical complexity and conservatism. To deal with the nonlinearities related to the unmeasurable real-time variation in the vehicle longitudinal speed and tire slip velocities in front and rear wheels, the Takagi–Sugeno (T-S) fuzzy form was undertaken for the observer design. The input-to-state stability (ISS) of the estimation errors was exploited using Lyapunov stability arguments to allow for more relaxation and an additional robustness guarantee with respect to the disturbance term of unmeasurable nonlinearities. For the design of the LPV interconnected UIO, sufficient conditions of the ISS property were formulated as an optimization problem in terms of linear matrix inequalities (LMIs), which can be effectively solved with numerical solvers. Extensive experiments were carried out under various driving test scenarios, both in interactive simulations performed with the well-known Sherpa dynamic driving simulator, and then using the LAMIH Twingo vehicle prototype, in order to highlight the effectiveness and the validity of the proposed observer design.

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Section: Related Work On Virtual Sensorsmentioning

confidence: 99%

Section: Ni-uio Stability and Convergence Analysismentioning

confidence: 99%

Virtual Sensor: Simultaneous State and Input Estimation for Nonlinear Interconnected Ground Vehicle System Dynamics

Sentouh

Fouka

Popieul

2023

Sensors

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“…There are many sources of errors that can compromise the efficiency of the dynamic model, such as speed sensors noise, difference of wheels radii, and position of the GPS antenna in relation to CG. Several studies to estimate the radius of wheels [3,13] confirm the impact of this factor over the position accuracy. Thus, our focus is to overcome this source of error using a technique to estimate this difference dynamically.…”

Section: Dynamic Modelmentioning

confidence: 99%

Dead reckoning using time series regression models

Neto

Mitton

Campista

et al. 2018

Proceedings of the 4th ACM MobiHoc Workshop on Experiences With the Design and Implementation of Smart Objects

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Connected car technology promises to drastically reduce the number of accidents involving vehicles. Nevertheless, this technology requires the vehicle precise location to work. The adoption of Global Positioning System (GPS) as a navigation device imposes limitations to geolocation information under non-line-of-sight conditions. This work introduces the Time Series Dead Reckoning System (TedriS) as a solution for dead reckoning navigation when the GPS fails. TedriS uses Time Series Regression Models (TSRM) and the data from the rear wheel speed sensor of the vehicle to estimate the absolute position. The process to estimate the position is carried out in two phases: training and predicting. In the training phase, a novel technique applies TSRM and stores the relationship between the GPS and the rear wheel speed data; then in the predicting phase, this relationship is used. We analyze TedriS using traces collected at the campus of Federal University of Rio de Janeiro (UFRJ), Brazil, and with indoor experiments with a robot. Results show an accuracy compatible with dead-reckoning navigation state-of-art systems.

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