A novelGlobal Chassis Control(GCC) system based on a multilayer architecture with three levels: top: decision layer, middle: control layer, and bottom: system layer is presented. The main contribution of this work is the development of a data-based classification and coordination algorithm, into a single control problem. Based on a clustering technique, the decision layer classifies the current driving condition. Afterwards, heuristic rules are used to coordinate the performance of the considered vehicle subsystems (suspension, steering, and braking) using local controllers hosted in the control layer. The control allocation system uses fuzzy logic controllers. The performance of the proposed GCC system was evaluated under different standard tests. Simulation results illustrate the effectiveness of the proposed system compared to an uncontrolled vehicle and a vehicle with a noncoordinated control. The proposed system decreases by 14% the braking distance in the hard braking test with respect to the uncontrolled vehicle, the roll and yaw movements are reduced by 10% and 12%, respectively, in the Double Line Change test, and the oscillations caused by load transfer are reduced by 7% in a cornering situation.
A method for modeling anElectrorheological(ER) damper is proposed. The modeling method comprehends two simple steps: characterization and model customization. These steps are based on the experimental data of the damper behavior. Experiments were designed to explore the nonlinear behavior of the damper at different frequencies and actuation signals (i.e., automotive domain). The resulting model has low computational complexity. The method was experimentally validated with a commercial damper. Theerror-to-signal Ratio(ESR) performance index was used to evaluate the model accuracy. The results were quantitatively compared with two well-known ER damper models: theChoiparametric model and theEyring-plasticmodel. The new proposed model has a 44% better ESR index than theChoiparametric model and 28% for theEyring-plasticmodel. A qualitative comparison based on density plots highlights the advantages of this proposal.
The influence of magneto-rheological damper modeling in vehicle dynamics analysis is studied. Several tests using CarSim TM compare a four-corner controlled semi-active suspension for two different magneto-rheological damper models. The magneto-rheological damper characteristics were identified from experimental data. A model-free controller discards the influence of control and emphasizes the compliance of the magneto-rheological damper model; the characteristics of the vehicle index performance considered were comfort, road holding, handling, roll and suspension deflection. The comparison for magneto-rheological damper dynamics and semi-active suspension covers the automotive bandwidth. The results show that high precision of a magneto-rheological damper model as an isolated feature is not enough. The magneto-rheological damper model, as a component of a vehicle suspension, needs to simulate with passive precision and variable damping forces. The findings exhibit the requisite of accurate models for evaluation of semi-active control systems in classic tests. The lack of the friction component in a magneto-rheological damper model leads to an overestimation in handling and stability.
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