Robert Babuska

Bio: Robert Babuska is an academic researcher from Delft University of Technology. The author has contributed to research in topics: Fuzzy logic & Reinforcement learning. The author has an hindex of 56, co-authored 371 publications receiving 15388 citations. Previous affiliations of Robert Babuska include Carnegie Mellon University & Czech Technical University in Prague.

Fuzzy Gain-Scheduled H-infinity Flight Control Law Design

Abstract: For most of the FBW aircraft flying today, the control laws have been developed by using classical single-loop frequency responses and root locus design techniques. Moreover, gain scheduling is the most common systematic approach to cope with the nonlinearity of the aircraft dynamics over the flight envelope. However, the singleloop design procedure as well as the identification of the operating points and the design of the interpolation scheme are time-consuming processes. One way to circumvent the single-loop design approach is to apply a multivariable H∞ control design technique, while the identification of the operating points and the design of the interpolating scheme for gain scheduling can be done automatically by a fuzzy clustering approach. Both techniques have been successfully evaluated during pilot-in-the-loop simulations. The main contribution of this paper is a first step to combine the gain scheduling technique based on fuzzy clustering and the H∞ design approach. The H∞ controllers are designed locally in a number of operating points and the interpolation between them (scheduling) takes place through fuzzy membership functions. The gain-scheduled H∞ controller performs satisfactorily. Moreover, the performance using the gain∗PhD student, Control Systems Engineering, Faculty of Information Technology and Systems, Delft University of Technology. Adress: Mekelweg 4, NL-2628 CD Delft, The Netherlands. Phone: +31-15278-3371. Fax: +31-15-278-6679. E-mail: m.oosterom@its.tudelft.nl †PhD, Center for Applied Autonomous Sensor Systems, Orebro University, Sweden. ‡Professor, Control Systems Engineering Laboratory, Faculty of Information Technology and Systems, Delft University of Technology. Copyright c ©2002 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. scheduled H∞ controller is significantly improved compared to the performance using the fixed H∞ controller.

A Fuzzy Set Theoretical Approach to the Automatic Generation of Absenteeism Analyses in Natural Language

Abstract: In this contribution a first important step is made towards the automatic generation of reports that describe (in natural language) absenteeism because of sickness in companies. Three parameters are examined: the sickness percentage, the absenteeism percentage (because of sickness), and the reintegration percentage. By means of fuzzy set techniques and an index of reintegration they are transformed into a linguistic description. The resulting technique has been successfully applied in the analysis of numerical absenteeism data of the year 2000 in about 20 divisions of a big company in the Netherlands.

On the convergence of Ant Colony Optimization with stench pheromone

Abstract: Ant Colony Optimization (ACO) has proved to be a powerful metaheuristic for combinatorial optimization problems. From a theoretical point of view, the convergence of the ACO algorithm is an important issue. In this paper, we analyze the convergence properties of a recently introduced ACO algorithm, called ACO with stench pheromone (ACO-SP), which can be used to solve dynamic traffic routing problems through finding the minimum cost routes in a traffic network. This new algorithm has two different types of pheromone: the regular pheromone that is used to attract artificial ants to the arc in the network with the lowest cost, and the stench pheromone that is used to push ants away when too many ants converge to that arc. As a first step of a convergence proof for ACO-SP, we consider a network with two arcs. We show that the process of pheromone update will transit among different modes, and finally stay in a stable mode, thus proving convergence for this given case.

Nonlinear model predictive control of a GDI engine

Abstract: A model predictive approach to the control of a GDI engine is presented. Fuzzy Takagi-Sugeno type models are used to predict the future engine behaviour. The optimization algorithm is based on instantaneous linearization of the nonlinear prediction model at the current operating point. Special mode switching strategies are designed to minimize the torque bumps during combustion mode changes. The performance of the controller has been evaluated on the European driving cycle using a dynamic simulation model, including powertrain, chassis and driver's submodels. Results have been achieved that show the applicability of the approach to the control of GDI engines.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

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.

Reinforcement learning in robotics: A survey

Abstract: Reinforcement learning offers to robotics a framework and set of tools for the design of sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic problems provide both inspiration, impact, and validation for developments in reinforcement learning. The relationship between disciplines has sufficient promise to be likened to that between physics and mathematics. In this article, we attempt to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots. We highlight both key challenges in robot reinforcement learning as well as notable successes. We discuss how contributions tamed the complexity of the domain and study the role of algorithms, representations, and prior knowledge in achieving these successes. As a result, a particular focus of our paper lies on the choice between model-based and model-free as well as between value-function-based and policy-search methods. By analyzing a simple problem in some detail we demonstrate how reinforcement learning approaches may be profitably applied, and we note throughout open questions and the tremendous potential for future research.