The enhancement of the passengers comfort and their safety are part of the constant concerns for car manufacturers. As a solution, the semi-active damping control systems have emerged to adapt the suspension features, where the road profile is one of the most important factors that determine the automotive vehicle performance. Because direct measurements of the road condition represent expensive solutions and, are susceptible to be contaminated, this paper proposes a novel road profile estimator that offers the essential information (road roughness and its frequency) for the adjustment of the vehicle dynamics by using conventional sensors of cars. Based on the Q-parametrization approach, an adaptive observer estimates the dynamic road signal, posteriorly, a Fourier analysis is used to compute online the road roughness condition and perform an ISO 8608 classification. Experimental results on the rear-left corner of a 1:5 scale vehicle, equipped with Electro-Rheological (ER) dampers, have been used to validate the proposed road profile estimation method. Different ISO road classes evaluate online the performance of the road identification algorithm, whose results show that any road can be identified successfully at least 70% with a false alarm rate lower than 5%; the general accuracy of the road classifier is 95%. A second test with variable vehicle velocity shows the importance of the online frequency estimation to adapt the road estimation algorithm to any driving velocity, in this test the road is correctly estimated 868 of 1,042 m (error of 16.7%). Finally, the adaptability of the parametric road estimator to the semi-activeness property of the ER damper is tested at different damping coefficients.
,luc.dugard}@gipsa-lab.grenoble-inp.fr A Magneto-Rheological (MR) damper is evaluated under exhaustive experimental scenarios, generating a complete database. The obtained database includes classical tests and new proposals emphasizing the frequency contents. It also includes the impact of the electric current fluctuations. The variety of the performed experiments allows to study the MR damper force dynamics. A brief description of the damper behavior and a categorization of experiments based on driving conditions and target applications on vehicle dynamics is discussed. The identification of two MR damper models as well as their cross validation emphasize the importance of the persistence of experimental inputs and the combinations of rod displacement and electric current sequences for better modeling. New findings in Design of Experiments for model identification are presented. 1. Mass production: Mechanical simplicity given that it is 1. The DoE for identification of MR damper models have not precisely been focused on a specific application.
Non-pathological mental fatigue is a recurring, but undesirable condition among people in the fields of office work, industry, and education. This type of mental fatigue can often lead to negative outcomes, such as performance reduction and cognitive impairment in education; loss of focus and burnout syndrome in office work; and accidents leading to injuries or death in the transportation and manufacturing industries. Reliable mental fatigue assessment tools are promising in the improvement of performance, mental health and safety of students and workers, and at the same time, in the reduction of risks, accidents and the associated economic loss (e.g., medical fees and equipment reparations). The analysis of biometric (brain, cardiac, skin conductance) signals has proven to be effective in discerning different stages of mental fatigue; however, many of the reported studies in the literature involve the use of long fatigue-inducing tests and subject-specific models in their methodologies. Recent trends in the modeling of mental fatigue suggest the usage of non subject-specific (general) classifiers and a time reduction of calibration procedures and experimental setups. In this study, the evaluation of a fast and short-calibration mental fatigue assessment tool based on biometric signals and inter-subject modeling, using multiple linear regression, is presented. The proposed tool does not require fatigue-inducing tests, which allows fast setup and implementation. Electroencephalography, photopletismography, electrodermal activity, and skin temperature from 17 subjects were recorded, using an OpenBCI helmet and an Empatica E4 wristband. Correlations to self-reported mental fatigue levels (using the fatigue assessment scale) were calculated to find the best mental fatigue predictors. Three-class mental fatigue models were evaluated, and the best model obtained an accuracy of 88% using three features, β/θ (C3), and the α/θ (O2 and C3) ratios, from one minute of electroencephalography measurements. The results from this pilot study show the feasibility and potential of short-calibration procedures and inter-subject classifiers in mental fatigue modeling, and will contribute to the use of wearable devices for the development of tools oriented to the well-being of workers and students, and also in daily living activities.
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.
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