scispace - formally typeset
Search or ask a question
Author

Ilan Kroo

Other affiliations: Ames Research Center
Bio: Ilan Kroo is an academic researcher from Stanford University. The author has contributed to research in topics: Aerodynamics & Optimization problem. The author has an hindex of 45, co-authored 150 publications receiving 6010 citations. Previous affiliations of Ilan Kroo include Ames Research Center.


Papers
More filters
Journal ArticleDOI
TL;DR: This work investigates techniques for high-level control that are scalable, reliable, efficient, and robust to problem dynamics, and devise a health monitoring policy and a control policy modification to improve performance under endurance constraints.
Abstract: Interest in control of multiple autonomous vehicles continues to grow for applications such as weather monitoring, geographical mapping fauna surveys, and extra-terrestrial exploration. The task of persistent surveillance is of particular significance in that the target area needs to be continuously surveyed, minimizing the time between visitations to the same region. This distinction from one-time coverage does not allow a straightforward application of most exploration techniques to the problem, though ideas from these methods can still be used. The aerial vehicle dynamic and endurance constraints add additional complexity to the autonomous control problem, whereas stochastic environments and vehicle failures introduce uncertainty. In this work, we investigate techniques for high-level control, that are scalable, reliable, efficient, and robust to problem dynamics. Next, we suggest a modification to the control policy to account for aircraft dynamic constraints. We also devise a health monitoring policy and a control policy modification to improve performance under endurance constraints. The Vehicle Swarm Technology Laboratory-a hardware testbed developed at Boeing Research and Technology, Seattle, WA, for evaluating a swarm of unmanned air vehicles-is then described, and these control policies are tested in a realistic scenario.

376 citations

01 Aug 1995
TL;DR: The collaborative architecture is developed and its mathematical foundation is presented and an example application is presented which highlights the potential of this method for use in large-scale design applications.
Abstract: Collaborative optimization is a design architecture applicable in any multidisciplinary analysis environment but specifically intended for large-scale distributed analysis applications. In this approach, a complex problem is hierarchically decomposed along disciplinary boundaries into a number of subproblems which are brought into multidisciplinary agreement by a system-level coordination process. When applied to problems in a multidisciplinary design environment, this scheme has several advantages over traditional solution strategies. These advantageous features include reducing the amount of information transferred between disciplines, the removal of large iteration-loops, allowing the use of different subspace optimizers among the various analysis groups, an analysis framework which is easily parallelized and can operate on heterogenous equipment, and a structural framework that is well-suited for conventional disciplinary organizations. In this article, the collaborative architecture is developed and its mathematical foundation is presented. An example application is also presented which highlights the potential of this method for use in large-scale design applications.

258 citations

Proceedings ArticleDOI
01 Sep 1996
TL;DR: The present investigation focuses on application of the collaborative optimization architecutre to the multidisciplinary design of a single-stage-to-orbit launch vehicle, demonstrating the difference between minimum weight and minimum cost concepts.
Abstract: Collaborative optimization is a new design architecture specifically created for large-scale distributed-analysis applications. In this approach, a problem is decomposed into a user-defined number of subspace optimization problems that are driven towards interdisciplinary compatibility and the appropriate solution by a system-level coordination process. This decentralized design strategy allows domain-specific issues to be accommodated by disciplinary analysts, while requiring interdisciplinary decisions to be reached by consensus. The present investigation focuses on application of the collaborative optimization architecutre to the multidisciplinary design of a single-stage-to-orbit launch vehicle. Vehicle design, trajectory, and cost issues are directly modeled. Posed to suit the collaborative architecture, the design problem is characterized by 95 design variables and 16 constraints. Numerous collaborative solutions are obtained. Comparison of these solutions demonstrates the influence which an a priori ascent-abort criterion has on development cost. Similarly, objective-function selection is discussed, demonstrating the difference between minimum weight and minimum cost concepts. The operational advantages of the collaborative optimization architecutre in a multidisciplinary design environment are also discussed.

236 citations

Journal ArticleDOI
Ilan Kroo1
TL;DR: Focusing on relatively high-aspect-ratio subsonic wings, the review suggests that opportunities for new concepts remain, but the greatest challenge lies in their integration with other aspects of the system.
Abstract: ▪ Abstract This article describes some of the fundamental ideas underlying methods for induced-drag prediction and reduction. A review of current analysis and design methods, including their development and common approximations, is followed by a survey of several approaches to lift-dependent drag reduction. Recent concepts for wing planform optimization, highly nonplanar surfaces, and various tip devices may lead to incremental but important gains in aircraft performance. Focusing on relatively high-aspect-ratio subsonic wings, the review suggests that opportunities for new concepts remain, but the greatest challenge lies in their integration with other aspects of the system.

231 citations

Proceedings ArticleDOI
01 Sep 1996
TL;DR: This paper compares this approach with other architectures, examines the details of the formulation, and some aspects of its performance, and proposes a particular version of the architecture to better accommodate the occurrence of multiple feasible regions.
Abstract: Collaborative optimization is a multidisciplinary design architecture that is well-suited to large-scale multidisciplinary optimization problems. This paper compares this approach with other architectures, examines the details of the formulation, and some aspects of its performance. A particular version of the architecture is proposed to better accommodate the occurrence of multiple feasible regions. The use of system level inequality constraints is shown to increase the convergence rate. A series of simple test problems, demonstrated to challenge related optimization architectures, is successfully solved with collaborative optimization.

209 citations


Cited by
More filters
Posted Content
TL;DR: This paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies which are adaptive, distributed, asynchronous, and verifiably correct.
Abstract: This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

2,198 citations

Book ChapterDOI
11 Dec 2012

1,704 citations

Journal ArticleDOI
TL;DR: This paper reviews two types of DSM, static and time-based DSMs, and four DSM applications, effective for integrating low-level design processes based on physical design parameter relationships and leads to conclusions regarding the benefits of DSMs in practice and barriers to their use.
Abstract: Systems engineering of products, processes, and organizations requires tools and techniques for system decomposition and integration. A design structure matrix (DSM) provides a simple, compact, and visual representation of a complex system that supports innovative solutions to decomposition and integration problems. The advantages of DSMs vis-a-vis alternative system representation and analysis techniques have led to their increasing use in a variety of contexts, including product development; project planning, project management, systems engineering, and organization design. This paper reviews two types of DSMs, static and time-based DSMs, and four DSM applications: (1) component-based or architecture DSM, useful for modeling system component relationships and facilitating appropriate architectural decomposition strategies; (2) team-based or organization DSM, beneficial for designing integrated organization structures that account for team interactions; (3) activity-based or schedule DSM, advantageous for modeling the information flow among process activities; and (4) parameter-based (or low-level schedule) DSM, effective for integrating low-level design processes based on physical design parameter relationships. A discussion of each application is accompanied by an industrial example. The review leads to conclusions regarding the benefits of DSMs in practice and barriers to their use. The paper also discusses research directions and new DSM applications, both of which may be approached with a perspective on the four types of DSMs and their relationships.

1,580 citations

Journal ArticleDOI
01 Jan 2006
TL;DR: This work reviews the state-of-the-art metamodel-based techniques from a practitioner's perspective according to the role of meetamodeling in supporting design optimization, including model approximation, design space exploration, problem formulation, and solving various types of optimization problems.
Abstract: Computation-intensive design problems are becoming increasingly common in manufacturing industries. The computation burden is often caused by expensive analysis and simulation processes in order to reach a comparable level of accuracy as physical testing data. To address such a challenge, approximation or metamodeling techniques are often used. Metamodeling techniques have been developed from many different disciplines including statistics, mathematics, computer science, and various engineering disciplines. These metamodels are initially developed as “surrogates” of the expensive simulation process in order to improve the overall computation efficiency. They are then found to be a valuable tool to support a wide scope of activities in modern engineering design, especially design optimization. This work reviews the state-of-the-art metamodel-based techniques from a practitioner’s perspective according to the role of metamodeling in supporting design optimization, including model approximation, design space exploration, problem formulation, and solving various types of optimization problems. Challenges and future development of metamodeling in support of engineering design is also analyzed and discussed.Copyright © 2006 by ASME

1,503 citations

Journal ArticleDOI
TL;DR: The assessment was completed by the Intergovernmental Panel on Climate Change (IPCC) with a primary aim of reviewing the current state of knowledge concerning the impacts of climate change on physical and ecological systems, human health, and socioeconomic factors as mentioned in this paper.
Abstract: Climate Change 1995 is a scientific assessment that was generated by more than 1 000 contributors from over 50 nations. It was jointly co-ordinated through two international agencies; the World Meteorological Organization and the United Nations Environment Programme. The assessment was completed by the Intergovernmental Panel on Climate Change (IPCC) with a primary aim of reviewing the current state of knowledge concerning the impacts of climate change on physical and ecological systems, human health, and socioeconomic factors. The second aim was to review the available information on the technical and economic feasibility of the potential mitigation and adaptation strategies.

1,149 citations