Dirk Abel

Bio: Dirk Abel is an academic researcher from RWTH Aachen University. The author has contributed to research in topics: Control theory & Model predictive control. The author has an hindex of 19, co-authored 466 publications receiving 2813 citations. Previous affiliations of Dirk Abel include Siemens.

Simulation physiologischer Regelkreise mit der objektorientierten Modellbibliothek “HumanLib”

Abstract: Zusammenfassung Die “HumanLib” ist eine objektorientiert aufgebaute Modellbibliothek bestehend aus Komponenten des Herz-Kreislauf-Systems sowie körpereigenen Regelkreisen. Im Beitrag werden Aufbau und Modellierungsmethodik beschrieben. Anhand zweier Testszenarien werden Simulationsdaten mit Messdaten aus einem in vivo Versuch am Kalb sowie physiologischen Normalwerten verglichen. Abstract “HumanLib” is an object-oriented model library with components of the human cardiovascular system and physiological control mechanisms. In this paper, after specifying structure and modeling methods, simulated data is compared to measured data of an experimental calf model and to physiological textbook data.

Review on model predictive control: an engineering perspective

Abstract: Model-based predictive control (MPC) describes a set of advanced control methods, which make use of a process model to predict the future behavior of the controlled system. By solving a—potentially constrained—optimization problem, MPC determines the control law implicitly. This shifts the effort for the design of a controller towards modeling of the to-be-controlled process. Since such models are available in many fields of engineering, the initial hurdle for applying control is deceased with MPC. Its implicit formulation maintains the physical understanding of the system parameters facilitating the tuning of the controller. Model-based predictive control (MPC) can even control systems, which cannot be controlled by conventional feedback controllers. With most of the theory laid out, it is time for a concise summary of it and an application-driven survey. This review article should serve as such. While in the beginnings of MPC, several widely noticed review paper have been published, a comprehensive overview on the latest developments, and on applications, is missing today. This article reviews the current state of the art including theory, historic evolution, and practical considerations to create intuitive understanding. We lay special attention on applications in order to demonstrate what is already possible today. Furthermore, we provide detailed discussion on implantation details in general and strategies to cope with the computational burden—still a major factor in the design of MPC. Besides key methods in the development of MPC, this review points to the future trends emphasizing why they are the next logical steps in MPC.

Adaptive EKF-Based Vehicle State Estimation With Online Assessment of Local Observability

Abstract: In this paper, an extended Kalman filter-based estimator adopting a dynamic vehicle model for determining the vehicle’s longitudinal and lateral velocity as well as the yaw rate is proposed. Two additional adaptation states are introduced to scale longitudinal and lateral tire forces if necessary to account for uncertainties in the tire/road contact. As excitation plays a vital role as far as observability is concerned, the suggested approach assesses local observability online and keeps an unobservable adaptation state constant by introducing the respective state as a virtual measurement variable when losing local observability. Furthermore, the filter is part of a Global Navigation Satellite System (GNSS)-based estimation framework. It exploits the availability of a GNSS-based horizontal velocity estimate instead of wheel speeds as aiding measurement, thus being independent of wheel slip. Experimental results for scenarios with different kinds of excitation show the effectiveness of the proposed estimator in the nominal as well as in the perturbed vehicle parameter case requiring filter adaptation.

Optimal vehicle dynamics control for combined longitudinal and lateral autonomous vehicle guidance

Abstract: This contribution proposes a model-based predictive control approach for combined longitudinal and lateral vehicle guidance. The controller, which has been designed for an automotive collision avoidance system, aims at following a desired evasion trajectory at the handling limits. Thereby, the trajectory following problem is decomposed in a path following and a velocity trajectory tracking problem using the wheel steering angle and the longitudinal acceleration as control inputs. There are two major advantages of this approach. First, the a priori knowledge of the evasion trajectory is explicitly incorporated into the computation of control inputs. Second, the combined transmission of longitudinal and lateral tire forces is considered in the sense of an integrated vehicle dynamics control approach. Experimental results show the potential of the introduced control scheme.

Model Predictive Control of a Parallel Hybrid Vehicle Drivetrain

Abstract: Hybrid vehicles gain importance and attention as the need for more fuel-efficient propulsion concepts increases. This article describes the modelling and the control of the longitudinal dynamics of a hybrid vehicle with a parallel configuration. The key aspect of the displayed hybrid concept is the smooth but quick transition between pure electrical driving and hybrid driving which has to occur without interruption of the demanded driving power and jerk. A model predictive control concept is presented for solving this task.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

The Design and Analysis of Experiments

Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

The Ensemble Kalman Filter: Theoretical formulation and practical implementation

Abstract: The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it. This paper reviews the important results from these studies and also presents new ideas and alternative interpretations which further explain the success of the EnKF. In addition to providing the theoretical framework needed for using the EnKF, there is also a focus on the algorithmic formulation and optimal numerical implementation. A program listing is given for some of the key subroutines. The paper also touches upon specific issues such as the use of nonlinear measurements, in situ profiles of temperature and salinity, and data which are available with high frequency in time. An ensemble based optimal interpolation (EnOI) scheme is presented as a cost-effective approach which may serve as an alternative to the EnKF in some applications. A fairly extensive discussion is devoted to the use of time correlated model errors and the estimation of model bias.