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American control Conference

作者： Daniel Adelberger Luigi del Re Institute for Design and Control of Mechatronical Systems Johannes Kepler University Linz Austria

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

Safety is a key requirement in vehicle design. Passive safety systems have been improved over decades and have been able to reduce enormously the severity of road accidents. Driver assistance systems have become more and more important in recent years, safety being one of the main goals, and in the case of an immediate collision danger some functions, like automated braking, further reduce the likelihood or severity of impacts. As a next step, automated steering will be added, i.e. the vehicle is expected not only to brake, but also to look for a safer or less dangerous alternative path. The corresponding control problem is enormously more complex, because it includes a navigation part. In this paper, we propose to state it in terms of optimal control with the task to follow a given route - the default trajectory - under normal conditions but to switch to a safer one once the Time-To-Collision (TTC) goes below a pre-defined threshold. To achieve this, we propose a two-layer structure, basically reflecting the navigation and control tasks. The upper layer observes the traffic participants, precomputes alternative trajectories, and determines the TTC for each of these alternatives. If the TTC along the default trajectory falls under the threshold, the safest lane is then selected as the reference. The next layer is then responsible to track the reference by NMPC, as the dynamics of the vehicle in a critical case cannot be treated as linear. A physics-based prediction method is used to determine possible future positions of surrounding road users. This prediction method is evaluated using data recorded at a junction to guarantee sufficient performance. In the end of this work various scenarios are presented to visualize the behaviour of the proposed assistant.

关键词： Prediction methods vehicle Passive safety system control task critical case navigation Road users trajectory Optimal control

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Emission aware eco-driving on country roads

Emission aware eco-driving on country roads

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ASME 2019 Dynamic systems and control Conference, DSCC 2019

作者： Deng, Junpeng Polterauer, Philipp Del Re, Luigi Institute for Design and Control of Mechatronical Systems Johannes Kepler University Linz Linz4040 Austria

ISBN: (纸本)9780791859148

Eco-driving is a way to improve performance – mainly energy consumption – of road vehicles by computing an optimal speed and gear shifting profile based on vehicle data and road profile, e.g. slopes or speed limits. It mainly focuses on long haul scenarios such as highways, considering longitudinal movement only. Lateral acceleration of a vehicle is a critical quantity both in terms of comfort and safety, but its impact on fuel consumption or emissions is rarely considered or believed to be limited [1], as it does not affect directly the operating point of the engine. However, on country roads which usually present much stronger curvatures, lateral acceleration may be a critical constraint. In this paper, the impact of lateral acceleration limits on optimal solutions to multi-objective eco-driving is investigated. It is found that it may play an even more critical role than longitudinal acceleration with respect to fuel consumption and NOx emission. As a consequence, the choice of limits to lateral acceleration on curvy roads should be set very carefully in order to achieve a balance between energy saving, drivers comfort and travel time. The results of this work are validated on a high-feasibility Hardware-in-the-loop (HIL) system calibrated with data from Real Driving Emissions tests. Copyright © 2019 ASME

关键词： Travel time

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Decision making through stochastic maneuver validation for overtaking on country roads

Decision making through stochastic maneuver validation for o...

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IEEE Conference on Decision and control

作者： Daniel Adelberger Meng Wang Luigi del Re Institute for Design and Control of Mechatronical Systems Johannes Kepler University Linz Austria Institute of Transportation and Planning TU Delft Delft Netherlands

ISBN: (数字)9781728174471

ISBN: (纸本)9781728174488

Driver assistance systems have become more and more important in recent years due to the increasing degree of automation in road traffic - especially with regard to safety. Often a driver's perception of a situation is prone to inaccuracy, particularly on country roads. Consequently, there is a high mortality rate due to frontal collisions with oncoming traffic (among others). One of the most dangerous maneuvers that leads to such conflicts is overtaking. Overtaking involves other traffic participants and therefore uncertainties exist. We cope with this challenge by introducing a system that evaluates the options a vehicle has during overtaking (completing or aborting the maneuver) using stochastic models of the surrounding traffic. The stochastic models are used to predict the movements of surrounding road users. As a next step, the general feasibility of the possible maneuvers is checked and rated. The results of this analysis can either be directly used for longitudinal control, be forwarded to a low level controller, or serve as a guideline for the decision-making process of a human driver.

关键词： Acceleration Roads Vehicles Stochastic processes Safety Optimization Decision making

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Self-tuning NMPC of an Engine Air Path

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IFAC-PapersOnLine 2020年第2期53卷 13870-13875页

作者： David Mendoza Patrick Schrangl William Ipanaqué Luigi del Re Institute for Design and Control of Mechatronical Systems Johannes Kepler University Linz Linz Austria Universidad de Piura Department of Electronics and Automation Urb. San Eduardo Piura Peru

Many automotive systems such as engines have manufacturing tolerances or change over time. This limits the performance of controllers tuned for the nominal case. A robust controller can not always overcome this performance gap. Against this background, in this work, we propose a self-tuning control strategy for an engine air path model obtained from data of a real engine and show its benefits setting. The self-tuning control consists of an online parameter estimation algorithm for polynomial non-linear autoregressive with exogenous input (PNARX) models and a nonlinear model predictive controller (NMPC) implemented by the continuation/generalized minimum residual (C/GMRES) algorithm. In a first step design of experiments (DOE) is utilized to identify a PNARX model offline from measurements performed on an engine test bed. A tracking NMPC is designed for this model and applied in simulation on the identified model. The control performance is assessed for the case of a wrong initial guess. It is shown that the resulting performance gap can be overcome by the online parameter estimation of a k-step prediction model with directional forgetting. An improved closed loop control performance of the air path model confirms the approach.

关键词： automotive control system identification adaptive control predictive control

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Reduction of transient soot emissions of a production Diesel engine using a fast soot sensor and closed loop control

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IFAC-PapersOnLine 2019年第5期52卷 159-164页

作者： Florian Meier Patrick Schrangl Luigi del Re Institute for Design and Control of Mechatronical Systems Johannes Kepler University Linz Austria

Unfavorable combustion conditions during engine transient operation cause significantly higher emissions of toxic pollutants in comparison to static conditions. A closed loop controller could help to reduce these emissions under real driving conditions, provided the necessary measurements are available, in the diesel case mainly soot and NOx. In this paper we analyze the potential improvement of raw soot emissions using fast measurements. To this end, we consider the cumulated emissions obtained with the standard ECU setting and both with a new, fast Laser Induced Incandescence (LII) sensor and a production opacimeter. First, an emission model based on fast dynamic soot measurements is used to estimate the ideal tradeoff between soot and NOx. Then a feedback controller acting on the rail pressure is used to approximate the theoretical target. Experimental results confirm that a high reduction in soot can be achieved with a very low price in terms of NOx (-50%/+5%).

关键词： Emissions Soot Reduction Optimal control Closed Loop control

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Prediction of postprandial glucose excursions in type 1 diabetes using control-oriented process models

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IFAC-PapersOnLine 2021年第15期54卷 466-471页

作者： D. Adelberger F. Reiterer P. Schrangl Ch. Ringemann T. Huschto L. del Re Institute for Design and Control of Mechatronical Systems Johannes Kepler University Linz Altenberger Strasse 69 A-4040 Linz Austria Nemak Linz GmbH A-4030 Linz Austria Magna Powertrain Engineering Center Steyr GmbH & Co KG A-4300 St. Valentin Austria Roche Diabetes Care GmbH D-68305 Mannheim Germany

Reliable prediction of future blood glucose (BG) values is of high relevance for diabetes patients, since it enables the use of predictive glucose alarms (warning the patient about impending situations with dangerously low or high BG), as well as of model-based algorithms for smart glucose control. control-oriented graybox process models have proven very suitable for such tasks, especially when identified on data from clinical trials under well-defined conditions. The current paper analyzes how such models can also be reliably parametrized using outpatient data of patients on multiple daily injection (MDI) therapy. A dedicated preprocessing algorithm is presented to look for suitable (i.e. complete and sensible) data segments that allow for a reliable system identification. The focus of the current paper is on the prediction of postprandial glucose trajectories, more specifically on predictions made exactly at the time of meal ingestion. This corresponds to a particularly challenging task, but one with high importance for the model-based optimization of insulin doses. It is demonstrated that the identified process models are a suitable choice for predicting such postprandial glucose excursions.

关键词： Diabetes Identification Grey box modeling Prediction Validation

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Markovian city-scale modelling and mitigation of micro-particles from tyres

arXiv

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arXiv 2021年

作者： Obereigner, Gunda Overko, Roman Yilmaz, Serife Crisostomi, Emanuele Shorten, Robert Institute for Design and Control of Mechatronical Systems Johannes Kepler University Linz Austria School of Electrical and Electronic Engineering University College Dublin County Dublin Dublin Ireland Education Faculty Department of Mathematics Education Mehmet Akif Ersoy University Burdur Turkey Department of Energy Systems Territory and Constructions Engineering University of Pisa Pisa Italy Dyson School of Design Engineering Imperial College London South Kensington London United Kingdom

The recent uptake in popularity in vehicles with zero tailpipe emissions is a welcome development in the fight against traffic induced airborne pollutants. As vehicle fleets become electrified, and tailpipe emissions become less prevalent, non-tailpipe emissions (from tires and brake disks) will become the dominant source of traffic related emissions, and will in all likelihood become a major concern for human health. This trend is likely to be exacerbated by the heavier weight of electric vehicles, their increased power, and their increased torque capabilities, when compared with traditional vehicles. While the problem of emissions from tire wear is well-known, issues around the process of tire abrasion, its impact on the environment, and modelling and mitigation measures, remain relatively unexplored. Work on this topic has proceeded in several discrete directions including: on-vehicle collection methods;vehicle tire-wear abatement algorithms and controlling the ride characteristics of a vehicle, all with a view to abating tire emissions. Additional approaches include access control mechanisms to manage aggregate tire emissions in a geofenced area with other notable work focussing on understanding the particle size distribution of tire generated PM, the degree to which particles become airborne, and the health impacts of tire emissions. While such efforts are already underway, the problem of developing models to predict the aggregate picture of a network of vehicles at the scale of a city, has yet to be considered. Our objective in this paper is to present one such model, built using ideas from Markov chains. Applications of our modelling approach are given toward the end of this note, both to illustrate the utility of the proposed method, and to illustrate its application as part of a method to collect tire dust particles. © 2021, CC BY.

关键词： Tires

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On-line Maneuver Identification in Highway Traffic Using Elastic Template Matching⁎

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IFAC-PapersOnLine 2018年第15期51卷 557-562页

作者： Tkachenko, Pavlo Zhou, Jinwei Gagliardi, Davide del Re, Luigi Institute for Design and Control of Mechatronical Systems Johannes Kepler University Linz Altenberger Strasse 69 LinzA-4040 Austria

Early identification and interpretation of traffic maneuvers will become key elements of modern driver assistance systems. Currently, only simple traffic situations can be detected by using a decision tree (DT) procedure in most cases. Even though this approach is straightforward, it still requires an expert to build a different DT for every maneuver. Moreover, for complex driving behaviours such simple logic identification does not work. In the present work we develop a template (or pattern) matching (TM) algorithm for maneuver recognition in highway traffic with a particular focus on a cut-in maneuver. By using maneuver patterns we are able to consider scenarios of arbitrary complexity and make the identification procedure more systematic and with minimal man expert involvement. The key element of the proposed TM algorithm is the settlement of a distance measure. We use dynamic time warping (DTW) method for an optimal non-linear alignment between two sequences under certain restrictions. It enables a correct assignment of the same maneuver but having different dynamics (speed, aggressiveness, etc.), and is robust to data with missing values. We illustrate the proposed method by cut-in detection from real traffic data and compare it to the standard DT identification. © 2018

关键词： Template matching

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Model based Diesel Injection Profile Optimization to Reduce Transient Soot Peaks

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IFAC-PapersOnLine 2018年第31期51卷 219-224页

作者： Großbichler, Martin Polterauer, Philipp Zhang, Zhen Waschl, Harald Institute for Design and Control of Mechatronical Systems Johannes Kepler University Linz Austria AVL Commercial Driveline & Tractor Engineering GmbH Austria

To meet current legislation limits, modern Diesel engines achieve low raw emission levels and utilize complex aftertreatment systems. Still, during fast transients undesired emission peaks may occur for both soot and NOx. These are caused by deviations in the in-cylinder conditions between the quasi steady engine calibration and the transient operation, e.g. during tip-ins. In this work a case study is performed to analyze the potential reduction of transient soot emissions during a specified engine maneuver. An additional target is to investigate potential benefits of a novel in-situ soot sensor based on the Laser Induced Incandescence (LII) principle which offers a high temporal resolution. Measurement data from a Diesel engine is used to develop time varying setpoint deviation models which will be utilized in a numerical optimization problem to determine an optimized injection profile. The modeling and optimization is carried out in parallel for both sensors and results are compared against each other. In the experimental validation the optimized injection profiles were capable of reducing undesired overshoots during transients with minimal impact on the torque response. Furthermore, the novel sensor allows to gain additional insights in the relation between input parameters and soot response, which is reflected by the obtained models and control action. © 2018

关键词： Diesel engines Dust Engine cylinders Ignition Laws and legislation Optimization Soot Combustion control Compression ignition Engine control Experimental validations High temporal resolution Laser induced incandescence Modeling and optimization Numerical optimizations

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Unsupervised Clustering of Highway Motion Patterns *

Unsupervised Clustering of Highway Motion Patterns *

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International Conference on Intelligent Transportation

作者： Pavlo Tkachenko Jinwei Zhou Luigi del Re Institute for Design and Control of Mechatronical Systems Johannes Kepler University Linz Linz Austria

Validation and verification of modern autonomous vehicles can be seen as a limiting factor on their way to public roads. The standard road testing is not affordable due to the infinite number of possible real-life situations. Therefore there is wide consensus that it must be complemented by virtual testing. However, also the latter one cannot be performed for all situations, so a finite catalogue of special test cases, so called scenarios, will be used for virtual testing. This catalogue is expected to offer a good coverage of the general intended use of the driving function under test. To this end, it makes sense to derive these scenarios from real data. In this paper we propose a systematic way for building up such a catalogue progressively using sensor data. We use a method based on a variant of an on-line k-means algorithm for time series clustering under their alignment using the dynamic time warping. The advantage of the proposed method is the intuitive representation of the scenarios enabling their easy interpretations. Using experimental data, this paper illustrates how such a catalogue is produced and how it can be used for further scenario detection, for example.

关键词： Testing Clustering algorithms Microsoft Windows Time series analysis Heuristic algorithms Autonomous vehicles

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