The vehicle sideslip angle is a physical variable strongly affected by stability and directional behavior of vehicles. As a consequence, it represents the very functional feedback for all the actual vehicle dynamics control systems. Current technological advances have shown that estimation of the sideslip angle is crucial for vehicular stability. The measurement of the vehicle sideslip angle can be obtained by means of devices that are very expensive and not functional for an easy installation on the car. In addition, the possibility to know this quantity with no measurement system makes the solution more robust and reliable thanks to the absence of physical components. Based on the named drawbacks, vehicle slip angle estimation has of late attracted many scholarly and industrial researchers. Presently, many vehicle sideslip angle estimation techniques can be found in literature and almost all are based on the extended Kalman filter algorithm, which is entirely based on local linearization of the nonlinear system. State-Dependent Riccati-Equation has recently emerged as a nonlinear filtering formulation but up to date, not much it has not been investigated in the field of the road vehicle dynamics.
In a recently published paper, Salvatore Strano and Mario Terzo from the Department of Industrial Engineering at University of Naples Federico II, Naples in Italy used nonlinear technique for the estimation of the vehicle sideslip angle as an alternative approach that is not based on local linearization techniques presently in use. The two researchers purposed to employ the State-Dependent Riccati-Equation nonlinear filtering formulation. Their work is currently published in the journal, Meccanica.
The research method used entailed utilization of a single track vehicle model for the synthesis of the estimator. Next, the researchers carried out simulations and conducted comparisons with the largely used Extended Kalman Filter. Eventually, the performance of the estimator was verified by means of experimental data acquired with an instrumented vehicle.
The authors observed that the state-dependent-Riccati-equation based technique was able to give an estimated sideslip angle that was fully in accordance with the measured one. This helped to prove the effectiveness of the proposed solution, regardless of its simplicity. Additionally, the two researchers noted that the filter could be easily adapted to different vehicles, tires and boundary conditions without a detailed knowledge of their characteristics and, differently to common approaches, with no tuning procedure required.
Salvatore Strano and Mario Terzo study has successfully presented a state-dependent-Riccati-equation based Kalman filter technique for the estimation of the vehicle sideslip angle. This proposed technique has been seen to capture all nonlinearities. Moreover, this technique possesses considerable contribution in vehicle dynamics control systems that require the knowledge of the sideslip angle. In conclusion, the state-dependent-Riccati-equation is a potential and valid alternative to the Extended Kalman Filter.
Salvatore Strano, Mario Terzo. Vehicle sideslip angle estimation via a Riccati equation based nonlinear filter. Meccanica (2017) volume 52, page 3513–3529
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