Kalman filter as a virtual sensor: applied to automotive stability systems

Wenzel, T.A.; Burnham, Keith J.; Blundell, Mike; Williams, Richard A

doi:10.1177/0142331207072990

Cited by 28 publications

(17 citation statements)

References 14 publications

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“…Venhovens and Naab [1] and Gustafsson et al [2] estimated the lateral acceleration, yaw velocity, tire force, and road tire friction coefficient by using Kalman filter (KF). Wenzel et al [3,4] estimate vehicle's state variables and parametric variables by adopting dual extended Kalman filter parallel estimating method. Some other scholars use Luenberger observer and sliding mode observer to estimate sideslip angle and yawing force.…”

Section: Introductionmentioning

confidence: 99%

Soft Sensor of Vehicle State Estimation Based on the Kernel Principal Component and Improved Neural Network

Liu

Yang

et al. 2016

Journal of Sensors

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In the car control systems, it is hard to measure some key vehicle states directly and accurately when running on the road and the cost of the measurement is high as well. To address these problems, a vehicle state estimation method based on the kernel principal component analysis and the improved Elman neural network is proposed. Combining with nonlinear vehicle model of three degrees of freedom (3 DOF), longitudinal, lateral, and yaw motion, this paper applies the method to the soft sensor of the vehicle states. The simulation results of the double lane change tested by Matlab/SIMULINK cosimulation prove the KPCA-IENN algorithm (kernel principal component algorithm and improved Elman neural network) to be quick and precise when tracking the vehicle states within the nonlinear area. This algorithm method can meet the software performance requirements of the vehicle states estimation in precision, tracking speed, noise suppression, and other aspects.

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Section: Introductionmentioning

confidence: 99%

Soft Sensor of Vehicle State Estimation Based on the Kernel Principal Component and Improved Neural Network

Liu

Yang

et al. 2016

Journal of Sensors

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“…Another is to use GPS and gyroscope to measure, but the cost is too high to promote [7,8] . In addition, some soft computing based on state estimation has good application in the field [9][10][11] . To solve the problem that vehicle side slip angle in vehicle control process is too difficult to measure on-line, the vehicle side slip angle's soft computing model is established with Kalman filter and driver-vehicle closed-loop system, based on parameter soft sensor theory and discrete signal filtering theory.…”

Section: Introductionmentioning

confidence: 99%

Soft Computing for Vehicle Side Slip Angle Based on Kalman Filter

Sun¹

2013

Proceedings of the 2013 International Conference on Remote Sensing,Environment and Transportation Engineering

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Abstract-To solve the problem that vehicle side slip angle in vehicle control process is too difficult to measure on-line, soft computing for vehicle side slip angle is established with Kalman filter and driver-vehicle closed-loop system, based on parameter soft sensor theory and discrete signal filtering theory. This measurement can realize one mapping from information set of yaw rate and lateral acceleration which is easy to measure to side slip angle. The contrastive results of simulation and road test verify the effectiveness and higher precision of this measurement. And the application of soft computing technology has provided a feasible and accuracy and effective way for the estimator design in vehicle control system. The realization of state estimation based on soft computing technology provides a theoretical basis on soft computing applied to vehicle stability control system.

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“…In many cases, observers must run in realtime, since the information they provide is used to control the actual system. Usually, the system models are simple, even linear, since their fast evaluation is needed in order to meet the above mentioned real-time requirement [5]. However, detailed models would provide more and more accurate information, thus enabling the implementation of more sophisticated control strategies.…”

Section: Introductionmentioning

confidence: 99%

Automotive observers based on multibody models and the extended Kalman filter

et al. 2011

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This work is part of a project aimed to develop automotive real-time observers based on detailed multibody models and the extended Kalman filter (EKF). In previous works, a four-bar mechanism was studied to get insight into the problem. Regarding the formulation of the equations of motion, it was concluded that the state-space reduction method known as matrix-R is the most suitable one for this application. Regarding the sensors, it was shown that better stability, accuracy and efficiency are obtained as the sensored magnitude is a lower derivative and when it is a generalized coordinate of the problem. In the present work, the automotive problem has been already addressed, through the selection of a Volkswagen Passat as a case-study. A model of the car containing fourteen degrees of freedom has been developed. The observer algorithm that combines the equations of motion and the integrator has been reformulated so that duplication of the problem size is avoided, in order to improve efficiency. A maneuver of acceleration from rest and double lane change has been defined, and tests have been run for the "prototype", the "model" and the "observer", all the three computational, with the model having 100 kg more than the prototype. Results have shown that good convergence is obtained, but the computational cost is high, still far from real-time performance.

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Kalman filter as a virtual sensor: applied to automotive stability systems

Cited by 28 publications

References 14 publications

Soft Sensor of Vehicle State Estimation Based on the Kernel Principal Component and Improved Neural Network

Soft Sensor of Vehicle State Estimation Based on the Kernel Principal Component and Improved Neural Network

Soft Computing for Vehicle Side Slip Angle Based on Kalman Filter

Automotive observers based on multibody models and the extended Kalman filter

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