Development of rational tyre models for vehicle dynamics control design and combined vehicle state/parameter estimation

2014 ◽  
Vol 65 (2/3) ◽  
pp. 144 ◽  
Author(s):  
S. Çağlar Başlamışlı
Keyword(s):  
Parameter Estimation ◽  
Vehicle Dynamics ◽  
Control Design ◽  
State Parameter ◽  
Vehicle Dynamics Control

A Passivity Based Decentralized Control Design Methodology With Application to Vehicle Dynamics Control

2011 ◽  
Vol 134 (1) ◽  
Author(s):  
Carlos Villegas ◽  
Martin Corless ◽  
Wynita Griggs ◽  
Robert Shorten
Keyword(s):  
Control Systems ◽  
Decentralized Control ◽  
Output Feedback ◽  
Vehicle Dynamics ◽  
Design Methodology ◽  
Basic Problem ◽  
Control Design ◽  
Feedback Controllers ◽  
Structural Uncertainties ◽  
Vehicle Dynamics Control

A basic problem in the design of control systems is the lack of simple effective methods for designing decentralized control systems that are robust with respect to certain types of structural uncertainties. Here, we present one such design methodology that is based upon the Kalman–Yakubovich–Popov Lemma. Advantages of this approach include the ease with which output feedback controllers can be designed, and the fact that the design methodology and uncertainties are expressed using classical frequency domain notions. We use our design technique to obtain an integrated chassis controller for application to automotive dynamics.

Direct Longitudinal Force Feedback for High-Performance Vehicle Dynamics Control Systems

2021 ◽  
Author(s):  
Giorgio Riva ◽  
Luca Mozzarelli ◽  
Matteo Corno ◽  
Simone Formentin ◽  
Sergio M. Savaresi
Keyword(s):  
Model Predictive Control ◽  
Control Systems ◽  
Predictive Control ◽  
Vehicle Dynamics ◽  
High Performance ◽  
Force Feedback ◽  
Longitudinal Force ◽  
Spiral Path ◽  
Comparable Performance ◽  
Vehicle Dynamics Control

Abstract State of the art vehicle dynamics control systems do not exploit tire road forces information, even though the vehicle behaviour is ultimately determined by the tire road interaction. Recent technological improvements allow to accurately measure and estimate these variables, making it possible to introduce such knowledge inside a control system. In this paper, a vehicle dynamics control architecture based on a direct longitudinal tire force feedback is proposed. The scheme is made by a nested architecture composed by an outer Model Predictive Control algorithm, written in spatial coordinates, and an inner longitudinal force feedback controller. The latter is composed by four classical Proportional-Integral controllers in anti-windup configuration, endowed with a suitably designed gain switching logic to cope with possible unfeasible references provided by the outer loop, avoiding instability. The proposed scheme is tested in simulation in a challenging scenario where the tracking of a spiral path on a slippery surface and the timing performance are handled simultaneously by the controller. The performance is compared with that of an inner slip-based controller, sharing the same outer Model Predictive Control loop. The results show comparable performance in presence of unfeasible force references, while higher robustness is achieved with respect to friction curve uncertainties.

scholarly journals Simultaneous estimation of land surface scheme states and parameters using the ensemble Kalman filter: identical twin experiments

2011 ◽  
Vol 15 (8) ◽  
pp. 2437-2457 ◽  
Author(s):  
S. Nie ◽  
J. Zhu ◽  
Y. Luo
Keyword(s):  
Parameter Estimation ◽  
Soil Moisture ◽  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Land Surface ◽  
Estimation Method ◽  
State Parameter ◽  
Hydraulic Parameters ◽  
Constrained Estimation ◽  
Estimated Parameters

Abstract. The performance of the ensemble Kalman filter (EnKF) in soil moisture assimilation applications is investigated in the context of simultaneous state-parameter estimation in the presence of uncertainties from model parameters, soil moisture initial condition and atmospheric forcing. A physically based land surface model is used for this purpose. Using a series of identical twin experiments in two kinds of initial parameter distribution (IPD) scenarios, the narrow IPD (NIPD) scenario and the wide IPD (WIPD) scenario, model-generated near surface soil moisture observations are assimilated to estimate soil moisture state and three hydraulic parameters (the saturated hydraulic conductivity, the saturated soil moisture suction and a soil texture empirical parameter) in the model. The estimation of single imperfect parameter is successful with the ensemble mean value of all three estimated parameters converging to their true values respectively in both NIPD and WIPD scenarios. Increasing the number of imperfect parameters leads to a decline in the estimation performance. A wide initial distribution of estimated parameters can produce improved simultaneous multi-parameter estimation performances compared to that of the NIPD scenario. However, when the number of estimated parameters increased to three, not all parameters were estimated successfully for both NIPD and WIPD scenarios. By introducing constraints between estimated hydraulic parameters, the performance of the constrained three-parameter estimation was successful, even if temporally sparse observations were available for assimilation. The constrained estimation method can reduce RMSE much more in soil moisture forecasting compared to the non-constrained estimation method and traditional non-parameter-estimation assimilation method. The benefit of this method in estimating all imperfect parameters simultaneously can be fully demonstrated when the corresponding non-constrained estimation method displays a relatively poor parameter estimation performance. Because all these constraints between parameters were obtained in a statistical sense, this constrained state-parameter estimation scheme is likely suitable for other land surface models even with more imperfect parameters estimated in soil moisture assimilation applications.

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