Jan Croes

Model-Based Physical System Deployment on Embedded Targets with Contract-Based Design

Designing model-based physical systems has growing demand in consequence of increasing system complexity. In particular, observers/estimators are extensively used for the applications requiring state or disturbance estimation. Designing and deploying such numerically intensive physical systems onto embedded targets is a challenging task that requires codesign among various stakeholders from different technical backgrounds.The most important challenge is to obtain a numeric behavior of the estimator from an embedded target, that is able to represent the physical system states/disturbance with an acceptable error margin. Moreover, this error margin needs to be decided by the stakeholders, which makes the overall embedded deployment a co-design problem. The main contribution of this paper is to investigate the cause of the estimation error of an estimator that is deployed to embedded targets. This error is studied in the form of precision loss in addition to the error originating in the decreasing estimator measurement frequency for the embedded targets. We propose Assume-Guarantee (A/G) contracts to reconcile the viewpoints of the stakeholders, who reside at different abstraction levels. The feasibility of the proposed physical system deployment method is presented by utilizing a model-based virtual sensor estimator deployment for embedded targets as a case study.

State Estimation: A Model-Based Approach to Extend Test Data Exploitation

Design models can drastically improve the applicability of testing and allow measuring previously unmeasurable quantities and designing reduced test configurations. A common workflow is followed: a multiphysics system model provides a prediction of the system states which is corrected by the estimation algorithms using the measurement data. The model can then generate data of the non-measurable quantities (e.g. virtual sensors). A wide range of models can be used, including analytical, 1D lumped parameter and 3D distributed parameter models. Key is that they are easy to evaluate and have a small number of states, while capturing the dominant physics. Novel model order reduction techniques enable the use of more complex models. A wide range of state estimation approaches has been developed such as the (linear, extended, unscented, …) Kalman Filter and the Moving Horizon Estimator. All approaches require a trade-off between accuracy and computational load so that conventional estimators must be tailored to deal with high-fidelity nonlinear models of industrial complexity. The approach is illustrated with two cases: the estimation of hard-to-measure vehicle body forces using the extended Kalman filter and the application to an electro-mechanical drivetrain subject to unknown input forces. Methodological aspects are evaluated and different estimators are compared.

A COMBINED 1D-3D MODELING APPROACH FOR THE DESIGN AND ANALYSIS OF COMPLEX MECHATRONIC SYSTEM

Nowadays, virtual prototyping is often incorporated in the design process to accelerate the development process of complex mechatronic systems [1]. This implies that the use of experimental campaigns has to be reduced and the manufacturer has to rely more on simulation tools [2]. In this paper, the on-going activities on the simulation and validation of combined 1D and 3D models, for the design and analysis of complex mechatronic system, have been presented. This includes the development of a flexible multibody model, a lumped parameter driveline model and a control system. In order to exhibit the potential of the virtual design and analysis process for modern mechatronic systems, an industrial machine tool is used as a case study. For predicting the dynamic behavior of the machine, forecasting the influence of specific design changes, and assessing the impact of different control architectures with full confidence, the model needs to be validated. To this end, the simulation results of the virtual model is compared with the results obtained on the physical prototype.