A new on-line exponential parameter estimator without persistent excitation
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TLDR
In this paper, the authors propose a new algorithm that estimates on-line the parameters of a classical vector linear regression equation Y = Ω θ, where Y ∈ R n, Ω ∈ r n × q are bounded, measurable signals and θ ∈ ρ ∈ q is a constant vector of unknown parameters, even when the regressor Ω is not persistently exciting.About:
This article is published in Systems & Control Letters.The article was published on 2022-01-01 and is currently open access. It has received 6 citations till now. The article focuses on the topics: Estimator & Bounded function.read more
Citations
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Journal ArticleDOI
A new least squares parameter estimator for nonlinear regression equations with relaxed excitation conditions and forgetting factor
TL;DR: In this article , a new high performance least squares parameter estimator is proposed, which is applicable to nonlinearly parameterized regressions verifying a monotonicity condition and to a class of systems with switched time-varying parameters.
Journal ArticleDOI
Unknown piecewise constant parameters identification with exponential rate of convergence
TL;DR: In this article , a truly online estimation algorithm based on a well-known DREM approach is proposed to detect switching time and preserve time alertness with adjustable detection delay, and the adaptive law is derived that provides global exponential convergence of the regression parameters estimates to their true values in case the regressor is finitely exciting somewhere inside the time interval between two consecutive parameters switches.
Proceedings ArticleDOI
Experimental Quadrotor Physical Parameters Estimation
TL;DR: In this paper , the experimental quadrotor physical parameters were identified using two versions of the recently proposed technique known as dynamic regressor extension and mixing (DREM), which preprocesses, algebraically and dynamically, the regressor to alleviate the persistency of excitation constraints.
Journal ArticleDOI
Disturbance Frequency Estimation for an LTV System
TL;DR: In this article , the authors consider the frequency estimation problem for a sinusoidal disturbance acting on a linear time-varying system, where only the input and output signals are available.
References
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System Identification: Theory for the User
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Introduction to Numerical Analysis
Josef Stoer,Roland Bulirsch +1 more
TL;DR: This well written book is enlarged by the following topics: B-splines and their computation, elimination methods for large sparse systems of linear equations, Lanczos algorithm for eigenvalue problems, implicit shift techniques for theLR and QR algorithm, implicit differential equations, differential algebraic systems, new methods for stiff differential equations and preconditioning techniques.
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Adaptive filtering prediction and control
Graham C. Goodwin,Kwai Sang Sin +1 more
TL;DR: This unified survey focuses on linear discrete-time systems and explores the natural extensions to nonlinear systems and summarizes the theoretical and practical aspects of a large class of adaptive algorithms.
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Adaptive Control: Stability, Convergence and Robustness
S. Shankar Sastry,Marc Bodson +1 more
TL;DR: In this paper, the deterministic theory of adaptive control (AC) is presented in an introduction for graduate students and practicing engineers, with a focus on basic AC approaches, notation and fundamental theorems, identification problem, model-reference AC, parameter convergence using averaging techniques, and robustness.
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Reinforcement Learning and Feedback Control: Using Natural Decision Methods to Design Optimal Adaptive Controllers
TL;DR: In this article, the authors describe the use of reinforcement learning to design feedback controllers for discrete and continuous-time dynamical systems that combine features of adaptive control and optimal control, which are not usually designed to be optimal in the sense of minimizing user-prescribed performance functions.