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Recursive least squares filter

About: Recursive least squares filter is a research topic. Over the lifetime, 8907 publications have been published within this topic receiving 191933 citations.


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Journal ArticleDOI
TL;DR: In this paper, an improved non-linear backstepping control scheme based on combination of recursive least squares (RLS) and differential evolution (DE) optimisation was proposed to regulate permanent magnet synchronous motor (PMSM) at a very low speed and control MEES device effectively.
Abstract: The spiral torsion spring-based mechanical elastic energy storage (MEES) device presented previously with inherent characteristic of simultaneous variations of inertia and torque is disadvantage to be actuated by conventional control method. This study proposes an improved non-linear backstepping control scheme based on combination of recursive least squares (RLS) and differential evolution (DE) optimisation to regulate permanent magnet synchronous motor (PMSM) at a very low speed and control MEES device effectively. For this presented control scheme, first, a single RLS method with forgetting factor is presented to simultaneously estimate the time-varying inertia and torque of MEES device. Second, on the basis of estimation results, an improved non-linear backstepping control algorithm considering parameter variations in derivation process is proposed to drive PMSM to operate at a very low speed. Finally, a self-adaptive parameter DE method is designed to optimise the several control parameters. The performance of the proposed methodology is verified through simulations and experiments.

36 citations

Journal ArticleDOI
TL;DR: One of the algorithms is a direct extension of the conventional RLS lattice adaptive linear filtering algorithm to the nonlinear case and the other is based on the QR decomposition of the prediction error covariance matrices using orthogonal transformations.
Abstract: This paper presents two computationally efficient recursive least-squares (RLS) lattice algorithms for adaptive nonlinear filtering based on a truncated second-order Volterra system model. The lattice formulation transforms the nonlinear filtering problem into an equivalent multichannel, linear filtering problem and then generalizes the lattice solution to the nonlinear filtering problem. One of the algorithms is a direct extension of the conventional RLS lattice adaptive linear filtering algorithm to the nonlinear case. The other algorithm is based on the QR decomposition of the prediction error covariance matrices using orthogonal transformations. Several experiments demonstrating and comparing the properties of the two algorithms in finite and "infinite" precision environments are included in the paper. The results indicate that both the algorithms retain the fast convergence behavior of the RLS Volterra filters and are numerically stable. >

36 citations

Journal ArticleDOI
TL;DR: A new cost function is proposed and a recursive method is derived for the estimation of Volterra kernel coefficients and an approximation of @?"[email protected]?norm is used to develop the recursive estimation method.

36 citations

Journal ArticleDOI
TL;DR: The convergence properties of the gradient algorithm are analyzed under the assumption that the gain tends to zero, and a main result is that the convergence conditions for the gradient algorithms are the same as those for the recursive least squares algorithm.
Abstract: Parameter estimation problems that can be formulated as linear regressions are quite common in many applications. Recursive (on-line, sequential) estimation of such parameters can be performed using the recursive least squares (RLS) algorithm or a stochastic gradient version of this algorithm. In this paper the convergence properties of the gradient algorithm are analyzed under the assumption that the gain tends to zero. The technique is the same as the so-called ordinary differential equation approach, but the treatment here is self-contained and includes a proof of the boundedness of the estimates. A main result is that the convergence conditions for the gradient algorithm are the same as those for the recursive least squares algorithm.

36 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202356
2022104
2021172
2020228
2019234
2018237