Joris De Cuyper

Off-line feed-forward and H∞ feedback control on a vibration rig

Abstract This paper discusses the improvement of the tracking accuracy on an automotive test rig by extending the industrial available off-line controller with an H ∞ feedback controller. The improvement is shown by comparing the tracking error for both control schemes: the industrial controller on the one hand and the industrial controller with H ∞ feedback controller on the other hand. The H ∞ controller is designed based on a mixed sensitivity approach. After a theoretical analysis, the results are illustrated by simulations and experimental results on one axis of a hydraulic test rig which is used in the automotive industry for vibration comfort evaluations of new vehicle prototypes.

Optimized Design Method of Vehicle Suspension Systems Using a Reverse Engineering Approach

Nowadays companies dealing with the automotive market, and in particular product designers, are facing with highly competitive environments though conflicting demands to deliver more complex products with increased quality in ever shorter development cycles. The usage of numerical simulations is therefore a confirmed technique going through the conceptual (1D) modeling towards a detailed digital mock-up to realize complex multibody (3D) simulations. The purpose of this paper is to develop a systematic design method for the suspension systems using CAE (Computer Aided Engineering) tools to investigate different properties which have influence on the ride & handling of a vehicle. Running a reverse engineering approach, the elasto-kinematic characteristics of different types of suspensions can be focused out and exported from a multibody environment (Virtual.Lab Motion has been chosen as example) as look-up tables to be read by a 1D multi-domain software (in the work here presented we used Imagine.Lab AMESim). Combined runs of sensitivity analyses and optimization cycles would then bring to the final goal of understanding the most suitable target behavior of the full system and the weight of different design variables on this, being hence able to directly address modifications of the global configuration.Copyright

Integration of Time Waveform Replication Process in a Multibody Software for Reverse Load Identification

In the automotive industry, the need to meet the durability requirements in a very early stage of the development of a new vehicle model is becoming more and more crucial. This is a key factor that can reduce the time to market and avoids modifying substantially the design if a component fails earlier than expected. This is also a challenging task for several reasons; in the early phase the primary design suffers from a lack of knowledge about the loads that the new vehicle will experience in its life. In literature ([1][2][5][6][7]) several methods have been proposed; for instance the so-called digital test track approach ([1]) is a CAE-based tool in which the vehicle and the road are modeled in a multibody environment together with a detailed representation of the tire and the driver in order to perform a replication of a test drive. This predictive method is very valuable but still requires a lot of information about the vehicle’s components that is usually not available at this stage of the vehicle development. On the other hand a pure test-based procedure suffers from other problems such as the need of a mule vehicle and long and costly test campaigns that need to be repeated at each component’s modification. A hybrid approach has then been proposed and implemented successfully by LMS on industrial size cases. This approach known as Time Waveform Replication (TWR) ([2]) relies on a set of test data and multibody model available from test drives carried out on a predecessor or a vehicle similar to the one that is being currently designed. The data collected on a road test is used to back-calculate the equivalent spindle displacements that will cause the same forces on the multibody model that are experienced in the test sessions. This approach has several beneficial aspects with respect to the two mentioned before. The tire model does not need to be accurate since the displacement are applied directly to the spindles (but the application can be easily extended to “road profile identification” if a detailed tire model is available). Moreover it is well known that if the forces measured at the spindles are applied directly to the unconstrained multibody model, it will result in an undesired drift of the model due to a mismatch in the mass and inertial properties between the real vehicle and its model. This is even more important when measured forces are applied to a new vehicle model that is only similar to the tested one. The TWR approach relies on a linearized model of the vehicle that is derived directly from its multibody representation. Then the spindle displacements are back-calculated by pseudo-inversion of the Frequency Response Function of the system and the application of the desired target signals. This method gives a direct result only if the system is linear; this is typically not the case in the field of vehicle dynamics where the geometry of the suspension, the non-linear properties of the dampers and bushings together with the intrinsic non-linear nature of the constrained equation of motion implies that the linearized model used by TWR is valid only for small changes to the configuration at the instant of linearization. To cope with this problem, the TWR sets up an iterative process that uses the output error to update the input. In case of high non-linearities or large changes in the configuration the linearized model can be also updated. In this paper the integration of the TWR process in a multibody code such as LMS Virtual.Lab Motion is described. In particular a new tool named LMS Motion-TWR has been developed. The application guides the engineer in setting up the models inputs and outputs, allows to drive the multibody code to compute the linearized model and the association between the test data and the numerical responses of the model. The computation of the driving signals is performed by TWR core solver as a background process allowing the user to focus on the analysis of the results rather than spending time in dealing with file conversion and transfer from one software to another as was done in the past. Moreover several post-processing tools are available such as time and frequency domain plots, RMS error and X-Y plots. Finally this paper describes the application of the tool in an industrial case scenario using a model of a quad. A quad was equipped with several sensors and driven on a test track. The collected data is then used in the Motion TWR software to compute the equivalent spindle displacements. Since some of the front suspension parts are modeled as flexible bodies the reverse load identification analysis is completed by a durability calculation.Copyright

A New Solution for CAE Process Automation and Customization: LMS Virtual.Lab Composer

Automation and customization capabilities are becoming one of the most important trends for CAE industry, allowing to fulfill some important needs like improving productivity, increasing value-added time and establishing best practices. The state of the art to meet these requirements is the usage of dedicated applications, also called “verticals”, which enable end-users to save time by automating repetitive tasks, as well as to capture best practices and to manage complex processes with simple, user-friendly interfaces. Within the LMS CAE development suite Virtual.Lab one could already create MS Visual Basic scripts and stand-alone executables, by either programming or macro journaling, where different expertise levels of VB programming were required depending on the complexity of the desired application.A new solution, called LMS Virtual.Lab Composer, takes it even further: it dramatically improves the development process of verticals by providing dedicated MS Visual Basic controls which allow non-expert users to easily build dedicated applications without need of any extensive programming knowledge. Two examples of verticals created with Virtual.Lab Composer for different multi-body technology based applications are shown in this paper: LMS Driving Dynamics for vehicle’s driving dynamic assessment and a tool for engine mount suspension design.Copyright

Reduced state space model for the substructuring of nonlinear multibody mechanisms

In the work presented in this paper we introduce a novel approach for reducing nonlinear models of mechanical systems organized in an open kinematic chain topology. Starting from the Equation of Motion (EoM) of a constrained mechanical system including different sources of non-linearities, and assuming that the location and type of the non-linear behavior is known, a state space realization is obtained through linearization and standard matricial computation. Hints to generate this distinction are here given and basis towards a general procedure are suggested. Singular perturbation is hence adopted to purge the non-linearly behaving states and to reduce the model’s configuration parameters. The nonlinear portion is maintained in the MB environment, finally linked in a coupled simulation with the reduced first order system.Copyright