Comparison of online-parameter estimation methods applied to a linear belt drive system

Beckmann, Daniel; Riva, Mauro Hernan; Dagen, Matthias; Ortmaier, Tobias

doi:10.1109/ecc.2016.7810312

Cited by 7 publications

(7 citation statements)

References 7 publications

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“…Convergence of carfollowing model parameters in estimation is significantly influenced by the sensitivity of the parameters with respect to the driving behavior captured by the dataset [26]. Specifically for online filtering methods, proper models of the system and measurement noises are important to the performance of the method [2].…”

Section: Related Workmentioning

confidence: 99%

Online Parameter Estimation Methods for Adaptive Cruise Control Systems

Wang

Gunter

Nice

et al. 2021

IEEE Trans. Intell. Veh.

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Modeling Adaptive Cruise Control (ACC) vehicles enables the understanding of the impact of these vehicles on traffic flow. In this work, two online methods are used to provide real time system identification of ACC enabled vehicles. The first technique is a recursive least squares (RLS) approach, while the second method solves a nonlinear joint state and parameter estimation problem via particle filtering (PF).We provide a parameter identifiability analysis for both methods to analytically show that the model parameters are not identifiable using equilibrium driving. The accuracy and computational runtime of the online methods are compared to a commonly used offline simulationbased optimization (i.e., batch optimization) approach. The methods are tested on synthetic data as well as on empirical data collected directly from a 2019 model year ACC vehicle using data from sensors that are part of the stock ACC system. The online methods are scalable and provide comparable accuracy to the batch method. RLS runs in real time and is two orders of magnitude faster than the batch method for modest sized (e.g., 15 min) datasets. The particle filter also runs in real-time, and is also suitable in streaming applications in which the datasets can grow arbitrarily large.

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Section: Related Workmentioning

confidence: 99%

Online Parameter Estimation Methods for Adaptive Cruise Control Systems

Wang

Gunter

Nice

et al. 2021

IEEE Trans. Intell. Veh.

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“…By defining ψ ( n ) = ( left left X false( n − 1 false) left1 A c false( n − 1 false) ) as the measurement vector and θ ^ = ( left left b ^ left1 c ^ ) as the estimated parameters vector, a KF for estimating b , c values at each iteration can be applied as given by Equation (6) (Beckmann et al, 2016):…”

Section: Qov Semi-active Suspension Problem Formulationmentioning

confidence: 99%

“…This requires: (a) an additional sensor that measures the unsprung mass acceleration; and (b) estimation of states and parameters at the same time. The most common solution proposed in the literature (see Beckmann et al (2016) and Wan et al (2000) as examples) is a joint-estimation KF or a dual-estimation KF.…”

Section: Qov Active Suspension Problem Formulationmentioning

confidence: 99%

Managing the handling–comfort contradiction of a quarter-car system using Kalman filter

Muhammed

Gavrilov

2021

Transactions of the Institute of Measurement and Control

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This article introduces a new approach to manage the “handling–comfort” contradiction of a vehicle suspension system. The idea is based on determining a specific time constant that reflects the percentage of suspension damping. This time constant is defined using the measurement of the sprung mass acceleration and the suspension deflection. A distinction is made between the control unit design for a semi-active suspension system and the control unit design for an active suspension system. The semi-active design is based on two sensors and a Kalman filter (KF), while the active design is based on three sensors and a dual-estimation KF. For active suspension, a third sensor was added to measure the acceleration of the unsprung mass. Simulation is carried out in Simulink and Simscape environments. The results of the proposed approach were compared with the results achieved by the hybrid-hook system. Simulation results showed a better efficiency of the proposed approach in driving safety during a “comfort” situation.

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“…Monteil and Bouroche point out that careful experimental design is necessary to enable the identifiability of some parameters depending on the car following dynamics [15]. In addition, noise characterization can be non-trivial when calibrating a filtering algorithm [16].…”

Section: Introductionmentioning

confidence: 99%

Estimating adaptive cruise control model parameters from on-board radar units

Wang,

Gunter,

Nice

et al. 2019

Preprint

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Two new methods are presented for estimating car-following model parameters using data collected from Adaptive Cruise Control (ACC) enabled vehicles. The vehicle is assumed to follow a constant time headway relative velocity model in which the parameters are unknown and to be determined. The first technique is a batch method that uses a least-squares approach to estimate the parameters from time series data of the vehicle speed, space gap, and relative velocity of a lead vehicle. The second method is an online approach that uses a particle filter to simultaneously estimate both the state of the system and the model parameters. Numerical experiments demonstrate the accuracy and computational performance of the methods relative to a commonly used simulation-based optimization approach. The methods are also assessed on empirical data collected from a 2019 model year ACC vehicle driven in a highway environment. Speed, space gap, and relative velocity data are recorded directly from the factory-installed radar unit via the vehicle's CAN bus. All three methods return similar mean absolute error values in speed and spacing compared to the recorded data. The least-squares method has the fastest run-time performance, and is up to 3 orders of magnitude faster than other methods. The particle filter is faster than real-time, and therefore is suitable in streaming applications in which the datasets can grow arbitrarily large.

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Comparison of online-parameter estimation methods applied to a linear belt drive system

Cited by 7 publications

References 7 publications

Online Parameter Estimation Methods for Adaptive Cruise Control Systems

Online Parameter Estimation Methods for Adaptive Cruise Control Systems

Managing the handling–comfort contradiction of a quarter-car system using Kalman filter

Estimating adaptive cruise control model parameters from on-board radar units

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