Performance of Sensitivity based NMPC Updates in Automotive Applications

In this work we consider a half car model which is subject to unknown but measurable disturbances. To control this system, we impose a combination of model predictive control without stabilizing terminal constraints or cost to generate a nominal solution and sensitivity updates to handle the disturbances. For this approach, stability of the resulting closed loop can be guaranteed using a relaxed Lyapunov argument on the nominal system and Lipschitz conditions on the open loop change of the optimal value function and the stage costs. For the considered example, the proposed approach is realtime applicable and corresponding results show significant performance improvements of the updated solution with respect to comfort and handling properties.Comment: 6 pages, 2 figure

Resampling-Methoden zur mse-Korrektur und Anwendungen in der Betriebsfestigkeit

Von sicherheitsrelevanten Bauteilen im Automobilbau verlangt man, dass beim Kunden bis zur Zeit/Strecke q0 höchstens ein Anteil p0 ausgefallen ist. Die Verifikation dieses Quantils geschieht in einer Reihe von Versuchen, bei denen die Bauteile mit einer typischen Kraft zyklisch belastet werden, bis ein gewisses, im Vorfeld festgelegtes, Schadensbild auftritt und die Anzahl Ti der Zyklen („Schwingspiele“) als Lebensdauer notiert wird. Typischerweise ist der Stichprobenumfang N dabei sehr gering (N < 10), während gleichzeitig ein extremes Quantil 0 p0 0, 1 verifiziert werden soll. Verwendet man als Lebensdauerverteilung eine Weibulloder Lognormalverteilung, so tritt in den Quantilschätzern ein deutlicher Bias auf, der beseitigt werden soll. Da es sich hierbei in der Regel um einen positiven Bias handelt, würde man Bauteile als serientauglich einstufen, obwohl sie möglicherweise deutlich unter den Vorgaben liegen. Die Berechnung von Konfidenzintervallen für Quantile geschieht über Delta-Methoden, die ebenfalls schlechte Resultate liefern (in Form einer zu geringen empirischen Signifikanz linksseiter Intervalle). Im Folgenden werden Verallgemeinerungen der Bootstrap- und Jackknife- Biaskorrektur vorgestellt, welche nicht nur versuchen den Bias zu beseitigen, sondern direkt den mittleren quadratischen Fehler des Schätzers weitestgehend zu reduzieren. Simulationsstudien zeigen, dass dies für geringe Stichprobenumfänge gelingt. Außerdem wird untersucht, inwiefern die Methode in Kombination mit der Bootstrap-Quantil-Methode einen verbesserten Intervallschätzer für Quantile liefert. Dabei werden simulierte Daten betrachtet, deren Parameter repräsentativ für Lebensdauerverteilungen von sicherheitsrelevanten Bauteilen sind

Optimal control methods for the calculation of invariant excitation signals for multibody systems

Input signals are needed for the numerical simulation of vehicle multibody systems. With these input data, the equations of motion can be integrated numerically and some output quantities can be calculated from the simulation results. In this work we consider the corresponding inverse problem: We assume that some reference output signals are available, typically gained by measurement and focus on the task to derive the input signals that produce the desired reference output in a suitable sense. If the input data is invariant, i.e., independent of the specific system, it can be transferred and used to excite other system variants. This problem can be formulated as optimal control problem. We discuss solution approaches from optimal control theory, their applicability to this special problem class and give some simulation results

Undesired drift of multibody models excited by measured accelerations or forces

In the ground vehicle industry it is often an important task to simulate full vehicle models based on the wheel forces and moments, which have been measured during driving over certain roads with a prototype vehicle. The models are described by a system of differential algebraic equations (DAE) or ordinary differential equations (ODE). The goal of the simulation is to derive section forces at certain components for a durability assessment. In contrast to handling simulations, which are performed including more or less complex tyre models, a driver model, and a digital road profile, the models we use here usually do not contain the tyres or a driver model. Instead, the measured wheel forces are used for excitation of the unconstrained model. This can be difficult due to noise in the input data, which leads to an undesired drift of the vehicle model in the simulation

MSB Simulation of a hexapod based suspension test rig

Testing a new suspension based on real load data is performed on elaborate multi channel test rigs. Usually wheel forces and moments measured during driving maneuvers are reproduced on the rig. Because of the complicated interaction between rig and suspension each new rig configuration has to prove its efficiency with respect to the requirements and the configuration might be subject to optimization. This paper deals with modeling a new rig concept based on two hexapods. The real physical rig has been designed and meanwhile built by MOOG-FCS for VOLKSWAGEN. The aim of the simulation project reported here was twofold: First the simulation of the rig together with real VOLKSWAGEN suspension models at a time where the design was not yet finalized was used to verify and optimize the desired properties of the rig. Second the simulation environment was set up in a way that it can be used to prepare real tests on the rig. The model contains the geometric configuration as well as the hydraulics and the controller. It is implemented as an ADAMS/Car template and can be combined with different suspension models to get a complete assembly representing the entire test rig. Using this model, all steps required for a real test run such as controller adaptation, drive file iteration and simulation can be performed. Geometric or hydraulic parameters can be modified easily to improve the setup and adapt the system to the suspension and the load data

Calculating invariant loads for system simulation in vehicle engineering

For the numerical simulation of a mechanical multibody system (MBS), dynamical loads are needed as input data, such as a road profile. With given input quantities, the equations of motion of the system can be integrated. Output quantities for further investigations are calculated from the integration results. In this paper, we consider the corresponding inverse problem: We assume, that a dynamical system and some reference output signals are given. The general task is to derive an input signal, such that the system simulation produces the desired reference output. We present the state-of-the-art method in industrial applications, the iterative learning control method (ILC) and give an application example from automotive industry. Then, we discuss three alternative methods based on optimal control theory for differential algebraic equations (DAEs) and give an overview of their general scheme

Customer loads correlation in truck engineering

Safety and reliability requirements on the one side and short development cycles, low costs and lightweight design on the other side are two competing aspects of truck engineering. For safety critical components essentially no failures can be tolerated within the target mileage of a truck. For other components the goals are to stay below certain predefined failure rates. Reducing weight or cost of structures often also reduces strength and reliability. The requirements on the strength, however, strongly depend on the loads in actual customer usage. Without sufficient knowledge of these loads one needs large safety factors, limiting possible weight or cost reduction potentials. There are a lot of different quantities influencing the loads acting on the vehicle in actual usage. These ‘influencing quantities’ are, for example, the road quality, the driver, traffic conditions, the mission (long haulage, distribution or construction site), and the geographic region. Thus there is a need for statistical methods to model the load distribution with all its variability, which in turn can be used for the derivation of testing specifications

Comparison of the solutions of the elastic and elastoplastic boundary value problems

In this article, we consider the quasistatic boundary value problems of linear elasticity and nonlinear elastoplasticity, with linear Hooke’s law in the elastic regime for both problems and with the linear kinematic hardening law for the plastic regime in the latter problem. We derive expressions and estimates for the difference of the solutions of both models, i.e. for the stresses, the strains and the displacements. To this end, we use the stop and play operators of nonlinear functional analysis. Further, we give an explicit example of a homotopy between the solutions of both problems