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Warren N. White

Researcher at Kansas State University

Publications -  55
Citations -  802

Warren N. White is an academic researcher from Kansas State University. The author has contributed to research in topics: Underactuation & Turbine. The author has an hindex of 14, co-authored 55 publications receiving 745 citations.

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Control of nonlinear underactuated systems

TL;DR: In this article, the authors introduce a new method to design control laws for nonlinear, underactuated systems, which produces an infinite-dimensional family of control laws, whereas most control techniques only produce finite-dimensional families.
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The Need for Comprehensive Personnel Preparation in Transition and Career Development: A Position Statement of the Division on Career Development and Transition

TL;DR: The Division on Career Development and Transition's position statement on preparing personnel in transition as discussed by the authors is organized into four sections: (a) the broader context for transition personnel preparation; (b) what should be taught; (c) recommendations for personnel preparation programs; and (d) implications for educational policy, practice, and future research.
Posted Content

Matching, linear systems, and the ball and beam

TL;DR: This paper describes one matching condition and an approach for finding all control laws that fit the condition and presents the results from an experiment on a nonlinear ball and beam system.
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Brief Matching, linear systems, and the ball and beam

TL;DR: In this article, the authors describe one matching condition and an approach for finding all control laws that fit the condition. And they present the results from an experiment on a nonlinear ball and beam system.
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A Maximum Power Tracking Technique for Grid-Connected DFIG-Based Wind Turbines

TL;DR: In this article, a maximum power tracking technique for doubly fed induction generator-based wind turbines is presented, in which the electrical torque is proportional to the square of the rotor speed and the proportional coefficient is adaptively adjusted in real-time through three control laws.