Applied System Identification

TL;DR: The results demonstrate that the six-degree-of-freedom trajectory of a passive spring-mounted range sensor can be accurately estimated from laser range data and industrial-grade inertial measurements in real time and that a quality 3-D point cloud map can be generated concurrently using the same data.

TL;DR: A data-driven, kernel-based method for approximating the leading Koopman eigenvalues, eigenfunctions, and modes in problems with high-dimensional state spaces is presented, using a set of scalar observables that are defined implicitly by the feature map associated with a user-defined kernel function.

TL;DR: This chapter is not meant to be an exhaustive primer on linear control theory, although key concepts from optimal control are introduced as needed to build intuition and demonstrate known optimal solutions to linear control problems.

TL;DR: A review of active control of flow-induced cavity oscillations is presented in this paper, with emphasis on experimental implementation of open-and closed-loop control approaches, as well as the characteristics of various actuators, flow sensing and measurement, and control methodologies employed to date.

TL;DR: This chapter is not meant to be an exhaustive primer on linear control theory, although key concepts from optimal control are introduced as needed to build intuition and demonstrate known optimal solutions to linear control problems.

TL;DR: The state of load testing research and practice is surveyed and current techniques that are used in the three phases of a load test are compared and contrast.

TL;DR: Embedded algorithms proposed in this study are used to autonomously determine the balcony's modal properties with network-derived results found to be comparable to those derived from traditional offline techniques.

TL;DR: In this paper, two complementary modal analysis methods are implemented: peak picking (PP) method in frequency domain and more advanced stochastic subspace identification method in time domain.