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.

Control-design methodology for drinking-water treatment processes

Abstract: The performance of a drinking-water treatment plant is determined by the control of the plant. To design the appropriate control system, a control-design methodology of five design steps is proposed, which takes the treatment process characteristics into account. For each design step, the necessary actions are defined. Using the methodology for the pellet-softening treatment step, a new control scheme for the pellet-softening treatment step has been designed and implemented in the full-scale plant. The implementation resulted in a chemical usage reduction of 15% and reduction in the maintenance effort for this treatment step. Corrective actions of operators are no longer necessary.

Minimization of cross-talk in a piezo inkjet printhead based on system identification and feedforward control

Abstract: The printing quality delivered by a drop-on-demand inkjet printhead is severely affected by the residual oscillations in an ink channel and the cross-talk between neighboring ink channels. For a single ink channel, our earlier contribution shows that the actuation pulse can be designed, using a physical model, to effectively damp the residual oscillations. It is not always possible to obtain a good physical model for a single ink channel. A physical model for a multi-input multi-output (MIMO) inkjet printhead is made even more sophisticated by the presence of the cross-talk effect. This paper proposes a system identification-based approach to build a MIMO model for an inkjet printhead. Additionally, the identified MIMO model is used to design new actuation pulses to effectively minimize the residual oscillations and the cross-talk. Using simulation and experimental results, we demonstrate the efficacy of the proposed method.

Modeling of Hot Rolling Industrial Process Using Fuzzy Logic.

Abstract: Steel making is known as a complex manufacturing industrial process. Automation of the process represents a challenge. Empirical mathematical modeling of the process was used to design mill equipment, ensure productivity and service quality. This modeling approach shows many problems associated to complexity and time consumption. Soft computing techniques show significant modeling capabilities on handling complex nonlinear systems modeling. In this paper, we explore the use of Takagi-Sugeno (TS) technique to develop fuzzy models for the Hot-Rolling industrial nonlinear process. We propose three models for the rolling force, torque and slab temperature. A set of rules and membership functions which represents the dynamical relationship between the input and output of these models shall be presented. The performance of the fuzzy models will be compared to the known empirical models for the hot rolling system. Experimental data measured from the Eregli Iron and Steel Factory in Turkey shall be used for the verification of the model outstanding performance.

Fuzzy control : theory and design

Abstract: As the need for more advanced and flexible controllers increases in industry, new control methods are being introduced and investigated. Recently, the merits of control based on fuzzy sets theory, so called fuzzy logic control, are being recognized by more companies and new products based on fuzzy logic control are appearing. This paper explains the basics of fuzzy control and gives an overview of various control schemes in which fuzzy logic controllers can be incorporated. Design and implementation issues concerning these controllers are discussed as well. Attention is paid not only to the advantages but also to the drawbacks of fuzzy control.

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.