M Maarten Steinbuch

Bio: M Maarten Steinbuch is an academic researcher from Eindhoven University of Technology. The author has contributed to research in topics: Control theory & Feed forward. The author has an hindex of 51, co-authored 630 publications receiving 11892 citations. Previous affiliations of M Maarten Steinbuch include Nanyang Technological University & Delft University of Technology.

Robust extremum seeking and speed ratio control for high-performance CVT operation

Abstract: The variator in a pushbelt continuously variable transmission (CVT) enables a stepless variation of the transmission ratio within a finite range. Nowadays, the variator is electronically controlled and the variator control objectives are twofold: 1) tracking a transmission ratio reference; 2) optimizing the variator efficiency. Recently, the extremum seeking control (ESC) technique is exploited in view of optimizing the variator efficiency, which only uses measurements from sensors that are standard. However, the operating conditions are fixed and tracking a transmission ratio reference is omitted, for simplicity. In this paper, extensions are proposed that overcome these limitations. This is achieved via the construction of a disturbance feedforward control design and a speed ratio control design. Experiments illustrate the effectiveness of these extensions when the operating conditions are varied and tracking a transmission ratio reference is required.

Tracking control for piecewise linear systems using an error space approach: A case-study in sheet control

Abstract: This paper presents the design of tracking controllers for piecewise linear systems, with application to sheet control in a printer paper path. The approach that we will take is based upon an error space approach, which is derived from linear systems theory. We will show that due to the discontinuity in the piecewise linear system, the resulting model in error space consists of both flow conditions, describing the dynamics in each regime, and jump conditions, describing the error dynamics at the switching boundaries. Two types of controllers are proposed that result in either full or partial linearization of the closed-loop error dynamics. To show the effectiveness of the control design approach in practice, the sheet controllers are implemented on an experimental paper path setup.

Frequency-domain mapping approach of stability bounds for loop shaping of bilateral controllers

Abstract: Bilateral control architectures include multiple control elements. In general, the relation between a single control element and the stability of the entire system is non-linear. Therefore, stability is standard evaluated a posteriori, rendering the control design process to be complex and highly iterative. A priori understanding of stability constraints would simplify the design of control elements and, as performance is fundamentally limited by stability, could provide specific guidelines whether and how performance of the bilateral teleoperation system can be optimized. This paper presents a numerical visualization method that enables stability-based control design using classical loopshaping techniques: Frequency-domain Mapping of Bilateral Stability (FMBS). Unlike current stability-based control design approaches, the FMBS method i) is not limited to a fixed control element, a fixed control architecture or system dynamics and ii) enables the implementation of all often used stability criteria. The advantages of the FMBS method are theoretically validated through the use of two test cases, extracted from literature. Using the FMBS method, it is shown that control elements can be redesigned to achieve superior performance.

Approximate realization with time delay

Abstract: This paper describes the application of an approximate realization algorithm to dynamical systems with a time delay. First, a well-known algorithm is presented to obtain an approximate realization from an impulse response sequence. Then the limitation that a time delay imposes on the accuracy of this algorithm is discussed, and it is pointed out that time delays should be explicitly taken into account. Therefore, a time delay estimation method is proposed that utilizes the presented approximate realization algorithm. Simulation results show that the method is likely to provide an accurate estimate for the time delay in a dynamical system

Essentials of Robust Control

Abstract: 1. Introduction. 2. Linear Algebra. 3. Linear Systems. 4. H2 and Ha Spaces. 5. Internal Stability. 6. Performance Specifications and Limitations. 7. Balanced Model Reduction. 8. Uncertainty and Robustness. 9. Linear Fractional Transformation. 10. m and m- Synthesis. 11. Controller Parameterization. 12. Algebraic Riccati Equations. 13. H2 Optimal Control. 14. Ha Control. 15. Controller Reduction. 16. Ha Loop Shaping. 17. Gap Metric and ...u- Gap Metric. 18. Miscellaneous Topics. Bibliography. Index.

Progress in electrical energy storage system: A critical review

Abstract: Electrical energy storage technologies for stationary applications are reviewed. Particular attention is paid to pumped hydroelectric storage, compressed air energy storage, battery, flow battery, fuel cell, solar fuel, superconducting magnetic energy storage, flywheel, capacitor/supercapacitor, and thermal energy storage. Comparison is made among these technologies in terms of technical characteristics, applications and deployment status.

A survey of iterative learning control

Abstract: This article surveyed the major results in iterative learning control (ILC) analysis and design over the past two decades. Problems in stability, performance, learning transient behavior, and robustness were discussed along with four design techniques that have emerged as among the most popular. The content of this survey was selected to provide the reader with a broad perspective of the important ideas, potential, and limitations of ILC. Indeed, the maturing field of ILC includes many results and learning algorithms beyond the scope of this survey. Though beginning its third decade of active research, the field of ILC shows no sign of slowing down.

Social LSTM: Human Trajectory Prediction in Crowded Spaces

Abstract: Pedestrians follow different trajectories to avoid obstacles and accommodate fellow pedestrians. Any autonomous vehicle navigating such a scene should be able to foresee the future positions of pedestrians and accordingly adjust its path to avoid collisions. This problem of trajectory prediction can be viewed as a sequence generation task, where we are interested in predicting the future trajectory of people based on their past positions. Following the recent success of Recurrent Neural Network (RNN) models for sequence prediction tasks, we propose an LSTM model which can learn general human movement and predict their future trajectories. This is in contrast to traditional approaches which use hand-crafted functions such as Social forces. We demonstrate the performance of our method on several public datasets. Our model outperforms state-of-the-art methods on some of these datasets. We also analyze the trajectories predicted by our model to demonstrate the motion behaviour learned by our model.

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.