The response of active safety systems of modern cars strongly depends on the estimation accuracy in the key motion states of the vehicle. One common limitation of current systems is the lack of adaptability in the parameters of the vehicle model that are usually treated as time-invariant, although they are not exactly known or are subject to temporal changes. As a direct consequence, time invariant-parameter control systems may achieve sub-optimal performance and/or deteriorate according to the driving con- ditions. This paper presents a non-linear model-based observer for combined estimation of motion states and tyre cornering stiffness. It is based on common onboard sensors, that is a lateral acceleration and yaw rate sensor, and it works during normal vehicle manoeuvering. The identification framework relies on an aug- mented Extended Kalman filter to deal with model parameter variability and noisy measurement input. Results are described to evaluate the performance and sensitivity of the proposed approach, showing an improvement in the estimation accuracy that can reach an order of magnitude compared to standard approaches.

Vehicle dynamics estimation via augmented Extended Kalman Filtering

Reina G.
;
Messina A.
2019

Abstract

The response of active safety systems of modern cars strongly depends on the estimation accuracy in the key motion states of the vehicle. One common limitation of current systems is the lack of adaptability in the parameters of the vehicle model that are usually treated as time-invariant, although they are not exactly known or are subject to temporal changes. As a direct consequence, time invariant-parameter control systems may achieve sub-optimal performance and/or deteriorate according to the driving con- ditions. This paper presents a non-linear model-based observer for combined estimation of motion states and tyre cornering stiffness. It is based on common onboard sensors, that is a lateral acceleration and yaw rate sensor, and it works during normal vehicle manoeuvering. The identification framework relies on an aug- mented Extended Kalman filter to deal with model parameter variability and noisy measurement input. Results are described to evaluate the performance and sensitivity of the proposed approach, showing an improvement in the estimation accuracy that can reach an order of magnitude compared to standard approaches.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11587/437812
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