Model Selection ensuring Practical Identifiability for Models of Electric Drives with Coupled Mechanics

Physically motivated models of electric drive trains with coupled mechanics are ubiquitous in industry for control design, simulation, feed-forward, model-based fault diagnosis etc. Often, however, the effort of model building prohibits these model-based methods. In this paper an automated model selection strategy is proposed for dynamic simulation models that not only optimizes the accuracy of the fit but also ensures practical identifiability of model parameters during structural optimization. Practical identifiability is crucial for physically motivated, interpretable models as opposed to pure prediction and inference applications. Our approach extends structural optimization considering practical identifiability to nonlinear models. In spite of the nonlinearity, local and linear criteria are evaluated, the integrity of which is investigated exemplarily. The methods are validated experimentally on a stacker crane

Performance Optimal and Robust Design of an Idle-Speed Controller Considering Physical Uncertainties

Modern passenger vehicles are equipped with a great number of control functions targeting versatile performance aspects like safe drive-ability, comfortable or sporty ride concerning assistance systems or a proper adjustment of engine control functions in order to prevent noise vibration and harshness issues. In this paper a methodology for a performance optimal and robust controller design is presented. This methodology is applied on a given idle-speed controller implementation using a detailed nonlinear drive train model in closed loop considering physical parameter uncertainties. The results are discussed with exemplary selected performance measures

Design of a PI-Controller Based on Time-Domain Specification Utilizing the Parameter Space Approach

In automotive application PI and PID controllers are widely used. Commonly the controller parametrization is performed in a heuristic manner in the vehicle at different operating points. Model-based approaches offer many advantages like a reduced effort of the design process and a more systematically investigation of the parameter set. Circumventing experiments at the vehicle is not feasible, however the goal is to achieve a significant reduction of this part of work. The aim of this paper is to find the controller parameter region, that ensures compliance with defined measures of the controller performance in time domain. On the basis of the parameter space approach these measures need to be transferred into the s-domain, which is shown exemplary for a second order system and a PI-controlled integrator system. The latter serves as a simple vehicle drive train model for the design of the engine idle speed controller

Maternal outcomes and risk factors for COVID-19 severity among pregnant women.

Pregnant women may be at higher risk of severe complications associated with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which may lead to obstetrical complications. We performed a case control study comparing pregnant women with severe coronavirus disease 19 (cases) to pregnant women with a milder form (controls) enrolled in the COVI-Preg international registry cohort between March 24 and July 26, 2020. Risk factors for severity, obstetrical and immediate neonatal outcomes were assessed. A total of 926 pregnant women with a positive test for SARS-CoV-2 were included, among which 92 (9.9%) presented with severe COVID-19 disease. Risk factors for severe maternal outcomes were pulmonary comorbidities [aOR 4.3, 95% CI 1.9-9.5], hypertensive disorders [aOR 2.7, 95% CI 1.0-7.0] and diabetes [aOR2.2, 95% CI 1.1-4.5]. Pregnant women with severe maternal outcomes were at higher risk of caesarean section [70.7% (n = 53/75)], preterm delivery [62.7% (n = 32/51)] and newborns requiring admission to the neonatal intensive care unit [41.3% (n = 31/75)]. In this study, several risk factors for developing severe complications of SARS-CoV-2 infection among pregnant women were identified including pulmonary comorbidities, hypertensive disorders and diabetes. Obstetrical and neonatal outcomes appear to be influenced by the severity of maternal disease