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 = Ωθ, where Y ∈ Rn, Ω∈Rn×q are bounded, measurable signals and θ∈ Rq is a constant vector of unknown parameters, even when the regressor 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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Journal ArticleDOI
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.
Journal ArticleDOI
Concurrent learning adaptive control of linear systems with exponentially convergent bounds
TL;DR: It is shown that a verifiable condition on the linear independence of the recorded data is sufficient to guarantee global exponential stability, which allows the development of adaptive controllers that ensure good tracking without relying on high adaptation gains, and can be designed to avoid actuator saturation.
Journal ArticleDOI
Performance Enhancement of Parameter Estimators via Dynamic Regressor Extension and Mixing
TL;DR: A new procedure to design parameter estimators with enhanced performance is proposed, which yields a new parameter estimator whose convergence is established without the usual requirement of regressor persistency of excitation.
Journal ArticleDOI
Composite learning robot control with guaranteed parameter convergence
Yongping Pan,Haoyong Yu +1 more
TL;DR: This paper provides the first result of parameter convergence without the PE condition for adaptive control of a general class of robotic systems by developing a composite learning robot control (CLRC) strategy to achieve fast and accurate parameter estimation under a condition termed interval excitation (IE) which is much weaker than thePE condition.
Journal ArticleDOI
A parameter estimation approach to state observation of nonlinear systems
Romeo Ortega,Alexey A. Bobtsov,Alexey A. Bobtsov,Anton A. Pyrkin,Anton A. Pyrkin,Stanislav Aranovskiy,Stanislav Aranovskiy +6 more
TL;DR: The proposed approach is shown to be applicable to position estimation of a class of electromechanical systems and to two assumptions related to solvability of a partial differential equation and the ability to estimate the unknown parameters.
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