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.

Inertia and Friction Estimation of a Velocity-Controlled Servo Using Position Measurements

Abstract: This paper proposes a method that estimates the parameters of a velocity-controlled servo. A proportional-integral controller, which uses only position measurements, closes the loop. The proposed approach uses the steady-state response produced by steps and sine-wave signals; they do not produce high levels of vibration on the servo compared with random signals commonly used with the least squares algorithm; moreover, it relies on simple numerical calculations. The method, which is called in the sequel as the steady-state response method (SSRM), consists of two steps. The first step uses three constant reference inputs in order to identify a constant disturbance and the viscous and Coulomb friction coefficients of the servo. In the second step, the SSRM estimates the servo inertia using a sine wave plus a constant signal as a velocity reference input and employs the estimate of the viscous friction coefficient obtained in the first step. Experiments on a testbed employing a brushless servomotor allow comparing the results obtained using the SSRM and those produced by a standard recursive least squares method (RLSM).

Tracking Time-Varying Coefficient-Functions

Abstract: SUMMARY A method for adaptive and recursive estimation in a class of non-linear autoregressive models with external input is proposed. The model class considered is conditionally parametric ARX-models (CPARX-models), which is conventional ARX-models in which the parameters are replaced by smooth, but otherwise unknown, functions of a low-dimensional input process. These coe$cient functions are estimated adaptively and recursively without specifying a global parametric form, i.e. the method allows for on-line tracking of the coe$cient functions. Essentially, in its most simple form, the method is a combination of recursive least squares with exponential forgetting and local polynomial regression. It is argued, that it is appropriate to let the forgetting factor vary with the value of the external signal which is the argument of the coe$cient functions. Some of the key properties of the modi"ed method are studied by simulation. Copyright ( 2000 John Wiley & Sons, Ltd.

Dynamic weighing under nonzero initial conditions

Abstract: The discrete output error (OE) model of second order is derived for the weighing system dynamics. Using the model and the recursive least squares (RLS) procedure, model parameters and then the mass being weighed can be estimated from a dynamic measurement signal of very short duration. The validity and the accuracy of this method are illustrated by digital simulation studies and real-life measurements. >

Selective-Tap Adaptive Filtering With Performance Analysis for Identification of Time-Varying Systems

Abstract: Selective-tap algorithms employing the MMax tap selection criterion were originally proposed for low-complexity adaptive filtering. The concept has recently been extended to multichannel adaptive filtering and applied to stereophonic acoustic echo cancellation. This paper first briefly reviews least mean square versions of MMax selective-tap adaptive filtering and then introduces new recursive least squares and affine projection MMax algorithms. We subsequently formulate an analysis of the MMax algorithms for time-varying system identification by modeling the unknown system using a modified Markov process. Analytical results are derived for the tracking performance of MMax selective tap algorithms for normalized least mean square, recursive least squares, and affine projection algorithms. Simulation results are shown to verify the analysis.

QRS detection using adaptive filters: A comparative study.

Abstract: Electrocardiogram (ECG) is one of the most important physiological signals of human body, which contains important clinical information about the heart. Monitoring of ECG signal is done through QRS detection. In this paper, an improved QRS detection algorithm, based on adaptive filtering principle, has been designed. Enumeration of the effectiveness of various LMS variants used in adaptive filtering based QRS detection algorithm has been done through fidelity parameters like sensitivity and positive predictivity. Whole family of LMS algorithm has been implemented for comparison. Sign-sign LMS, sign error LMS, basic LMS and normalized LMS are re-implemented, while variable leaky LMS, variable step-size LMS, leaky LMS, recursive least squares (RLS), and fractional LMS are novel combination presented in this paper. After analysis of the obtained results, performance of leaky-LMS algorithm is found to be the best with sensitivity, positive predictivity, and processing time of 99.68%, 99.84%, and 0.45 s respectively. Reported results are tested and evaluated over MIT/BIH arrhythmia database. Presented study also concludes that the performance of most of the variants gets affected due to low SNR but the Leaky LMS performs better even under heavy noise conditions.