A Novel Method for the Reconstruction of Road Profiles from Measured Vehicle Responses Based on the Kalman Filter Method

The estimation of the disturbance input acting on a vehicle from its given responses is an inverse problem.To overcome some of the issues related to ill-posed inverse problems,this work proposes a method of reconstructing the road roughness based on the Kalman filter method.A half-car model that considers both the vehicle and equipment is established,and the joint input-state estimation method is used to identify the road profile.The capabilities of this methodology in the presence of noise are numerically demonstrated.Moreover,to reduce the influence of the driving speed on the estimation results,a method of choosing the calculation frequency is proposed.A road vibration test is conducted to benchmark the proposed method.

Use of generated artificial road profiles in road roughness evaluation

In the evaluation of road roughness and its effects on vehicles response in terms of ride quality, loads induced on pavement, drivers＇ comfort, etc., it is very common to generate road profles based on the equation provided by ISO 8608 standard, according to which it is possible to group road surface profiles into eight different classes. However, real profiles are significantly different from the artificial ones because of the non-stationary fea- ture of the first ones and the not full capability of the ISO 8608 equation to correctly describe the frequency content of real road profiles. In this paper, the international roughness index, the frequency-weighted vertical acceleration awz according to ISO 2631, and the dynamic load index are applied both on artificial and real profiles, highlighting the different results obtained. The analysis carried out in this work has highlighted some limitation of the ISO 8608 approach in the description of performance and conditions of real pavement profiles. Furthermore, the different sensitivity of the various indices to the fitted power spectral density parameters is shown, which should be taken into account when performing analysis using artificial profiles.

Development of a Virtual Environment for Simulation of a 3D Road Profile Using OpenCRG and MATLAB GUI

In vehicle dynamics, there are wide applications concerning the simulation of vehicles on roads. These simulation applications relate to vehicle driving, ride comfort and durability. An accurate prediction of simulation results requires reliability and efficiency of road representations. The MATLAB graphical user interface module, called MATLAB GUI, is used to develop virtual simulation laboratories that allow the user to interact with a computer program using graphical objects. In this context, the aim of this article is to use the MATLAB OpenCRG suite of tools and the MATLAB GUI to develop a virtual environment for simulating a 3D road profile. A three-dimensional model of a pothole with variable parameters is developed and integrated into the 3D road profile.

Stochastic model predictive braking control for heavy-duty commercial vehicles during uncertain brake pressure and road profile conditions

When heavy-duty commercial vehicles(HDCVs)must engage in emergency braking,uncertain conditions such as the brake pressure and road profile variations will inevitably affect braking control.To minimize these uncertainties,we propose a combined longitudinal and lateral controller method based on stochastic model predictive control(SMPC)that is achieved via Chebyshev–Cantelli inequality.In our method,SMPC calculates braking control inputs based on a finite time prediction that is achieved by solving stochastic programming elements,including chance constraints.To accomplish this,SMPC explicitly describes the probabilistic uncertainties to be used when designing a robust control strategy.The main contribution of this paper is the proposal of a braking control formulation that is robust against probabilistic friction circle uncertainty effects.More specifically,the use of Chebyshev–Cantelli inequality suppresses road profile influences,which have characteristics that are different from the Gaussian distribution,thereby improving both braking robustness and control performance against statistical disturbances.Additionally,since the Kalman filtering(KF)algorithm is used to obtain the expectation and covariance used for calculating deterministic transformed chance constraints,the SMPC is reformulated as a KF embedded deterministic MPC.Herein,the effectiveness of our proposed method is verified via a MATLAB/Simulink and TruckSim co-simulation.

Developing the Model and Environment for Validation of a Class-8 Truck

Many studies have been conducted by analyzing crash data that included road profile, site conditions, vehicle configurations and weights, driver behavior, etc.. However, limited studies have been conducted evaluating the impact of these factors on crashes and/or rollover through simulations. This is mainly due to lack of availability of verified full vehicle flexible-body models. The verification process is costly as it requires instrumentation of a heavy vehicle, scanning of road surfaces, and collection of data by running the vehicle over different road conditions, performing various maneuvering, etc. This paper presents the reverse engineering process of a class-8 truck and validation of a full flexible-body simulation model of a Wabash 53-foot trailer against the strain data recoded from proving ground testing of an instrumented truck. Simulation results show that, with the exception of the noise from the strain gage data from instrumented test run at 30 mph, there is a good agreement in periodicity and relative amplitude with the ADAMS model. A comparison of strain data from the flex-body model and the instrumented truck shows that the modeling and verification approach presented in this paper can be confidently used to validate the full flexible-body models developed for specific analyses.