Journal ArticleDOI
Plant Friendly Input Design: Convex Relaxation and Quality
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TLDR
A SemiDefinite Programme is formulated using the theory of generalized Tchebysheff inequalities to derive tight bounds on the quality of relaxation and simulations show that the relaxation results in more plant friendly input signals.Abstract:
A common practice in a system identification exercise is to perturb the system of interest and use the resulting data to build a model. The problem of interest in this contribution is to synthesize an input signal that is maximally informative for generating good quality models while being “plant friendly,” i.e., least hostile to plant operation. In this contribution, limits on input move sizes are the plant friendly specifications. The resulting optimization problem is nonlinear and nonconvex. Hence, the original plant friendly input design problem is relaxed which results in a convex optimization problem. We formulate a SemiDefinite Programme using the theory of generalized Tchebysheff inequalities to derive tight bounds on the quality of relaxation. Simulations show that the relaxation results in more plant friendly input signals.read more
Citations
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Journal ArticleDOI
Application-Oriented Input Design in System Identification: Optimal Input Design for Control [Applications of Control]
TL;DR: In this paper, the authors present a model-based control design tool for identifying the necessary models for a given model-classification problem in the industrial domain, with the goal to handle the increasingly stringent conditions on cost and performance related to identifying the models.
Journal ArticleDOI
Towards Patient-Friendly Input Signal Design for Optimized Pain Treatment Interventions
TL;DR: In this article, the authors examine some of the challenges associated with generating input signals for identifying dynamics in pain treatment interventions while imposing "patient-friendly" constraints on the design, and suggest various approaches (leading ultimately to optimization-based formulations) to obtain input signals with desired spectral properties under timedomain constraints of importance to clinical practice.
Optimal Input Signal Design for Data-Centric Identification and Control with Applications to Behavioral Health and Medicine
TL;DR: This dissertation examines generating input signals for data-centric system identification by developing a novel framework of geometric distribution of regressors and time-indexed output points, in the finite dimensional space, to generate sufficient support for the estimator.
Proceedings ArticleDOI
Optimal input signal design for data-centric estimation methods
Sunil Deshpande,Daniel E. Rivera +1 more
TL;DR: The design of optimal input signals formulated to produce informative data to be used by local modeling procedures is examined and the resulting optimization problem is solved using semidefinite relaxation methods.
Journal ArticleDOI
Optimal Plant Friendly Input Design for System Identification
TL;DR: This work presents a convex relaxation to the problem of designing an informative input subject to input move size and output power constraints, finitely parametrized using ideas from Tchebycheff systems and reformulated as a SemiDefinite Programme.
References
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Book
Convex Optimization
Stephen Boyd,Lieven Vandenberghe +1 more
TL;DR: In this article, the focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them, and a comprehensive introduction to the subject is given. But the focus of this book is not on the optimization problem itself, but on the problem of finding the appropriate technique to solve it.
Book
Optimization by Vector Space Methods
TL;DR: This book shows engineers how to use optimization theory to solve complex problems with a minimum of mathematics and unifies the large field of optimization with a few geometric principles.
Book ChapterDOI
Graph Implementations for Nonsmooth Convex Programs
Michael C. Grant,Stephen Boyd +1 more
TL;DR: Graph implementations as mentioned in this paper is a generic method for representing a convex function via its epigraph, described in a disciplined convex programming framework, which allows a very wide variety of smooth and nonsmooth convex programs to be easily specified and efficiently solved.