scholarly journals Parallel Distributed Framework for State Space Adaptive Filter for Removal of PLI from Cardiac Signals

2021 ◽  
Vol 25 (3) ◽  
pp. 249-270
Author(s):  
Inam ur Rehman ◽  
◽  
Hasan Raza ◽  
Nauman Razzaq ◽  
◽  
...  
Keyword(s):  
State Space ◽  
Adaptive Algorithm ◽  
Reference Signal ◽  
Computational Cost ◽  
Frequency Component ◽  
Least Square ◽  
Recursive Least Square ◽  
Qualitative And Quantitative ◽  
Cardiac Signals ◽  
Power Line Interference

Cardiac signals are often corrupted by artefacts like power line interference (PLI) which may mislead the cardiologists to correctly diagnose the critical cardiac diseases. The cardiac signals like high resolution electrocardiogram (HRECG), ultra-high frequency ECG (UHF-ECG) and intracardiac electrograms are the specialized techniques in which higher frequency component of interest up to 1 KHz are observed. Therefore, a state space recursive least square (SSRLS) adaptive algorithm is applied for the removal of PLI and its harmonics. The SSRLS algorithm is an effective approach which extracts the desired cardiac signals from the observed signal without any need of reference signal. However, SSRLS is inherited computational heavy algorithm; therefore, filtration of increased number of PLI harmonics bestow an adverse impact on the execution time of the algorithm. In this paper, a parallel distributed SSRLS (PD-SSRLS) algorithm is introduced which runs the computationally expensive SSRLS adaptive algorithm parallely. The proposed architecture efficiently removes the PLI along with its harmonics even the time alignment among the contributing nodes is not the same. Furthermore, the proposed PD-SSRLS scheme provides less computational cost as compared to sequentially operated SSRLS algorithm. A comparison has been drawn between the proposed PD-SSRLS algorithm and sequentially operated SSRLS algorithm in term of qualitative and quantitative performances. The simulation results show that the proposed PD-SSRLS architecture provides almost same qualitative and quantitative performances than that of sequentially operated SSRLS algorithm with less computational cost.

scholarly journals Dynamic Model and Inverse Kinematic Identification of a 3-DOF Manipulator Using RLSPSO

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 416 ◽  
Author(s):  
Josias Batista ◽  
Darielson Souza ◽  
Laurinda dos Reis ◽  
Antônio Barbosa ◽  
Rui Araújo
Keyword(s):  
Dynamic Model ◽  
Inverse Kinematics ◽  
Lagrange Equation ◽  
Computational Cost ◽  
Dynamic Parameter ◽  
Search Space ◽  
Least Square ◽  
Identification Accuracy ◽  
Identification Algorithm ◽  
Recursive Least Square

This paper presents the identification of the inverse kinematics of a cylindrical manipulator using identification techniques of Least Squares (LS), Recursive Least Square (RLS), and a dynamic parameter identification algorithm based on Particle Swarm Optimization (PSO) with search space defined by RLS (RLSPSO). A helical trajectory in the cartesian space is used as input. The dynamic model is found through the Lagrange equation and the motion equations, which are used to calculate the torque values of each joint. The torques are calculated from the values of the inverse kinematics, identified by each algorithm and from the manipulator joint speeds and accelerations. The results obtained for the trajectories, speeds, accelerations, and torques of each joint are compared for each algorithm. The computational costs as well as the Multi-Correlation Coefficient ( R 2 ) are computed. The results demonstrated that the identification accuracy of RLSPSO is better than that of LS and PSO. This paper brings an improvement in RLS because it is a method with high complexity, so the proposed method (hybrid) aims to improve the computational cost and the results of the classic RLS.

An Adaptive Semiactive Control Algorithm for Vehicle Suspension Systems

2003 ◽  
Author(s):  
Xubin Song ◽  
Mehdi Ahmadian ◽  
Steve Southward
Keyword(s):  
Dynamic System ◽  
Adaptive Algorithm ◽  
Control Algorithm ◽  
Suspension System ◽  
Least Square ◽  
Vehicle Suspension ◽  
Semiactive Control ◽  
Vehicle Speed ◽  
Recursive Least Square ◽  
Line System

In general, a vehicle suspension system can be characterized as a nonlinear dynamic system that is subjected to unknown vibration sources, dependent on road roughness and vehicle speed. In this paper, we will present a nonlinear-model-based adaptive semiactive control algorithm developed for nonlinear systems exposed to broadband non-stationary random vibration sources that are assumed to be unknown or not measurable. If there exist unknown and/or varying parameters of the dynamic system such as mass and stiffness, then the adaptive algorithm can include a recursive least square (RLS) method for on-line system identification. Since the adaptive algorithm is developed for semiactive systems, stability is guaranteed based on the fact that the system is energy conservative. The convergence of the adaptive system, however is not guaranteed, and is investigated through a numerical approach for a specific case. The simulation results for a magneto-rheological seat suspension system with the suggested adaptive control are presented. The results are compared with low-damping and high-damping cases, as well æ other configurations of skyhook control, in order to show the extent of the procurement that can be expected with the suggested adaptive skyhook control provides a better broadbandk performance for the suspension, as compared to the other damping configurations that are included here.

scholarly journals Nonlinear State Space Modeling and System Identification for Electrohydraulic Control

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Jun Yan ◽  
Bo Li ◽  
Hai-Feng Ling ◽  
Hai-Song Chen ◽  
Mei-Jun Zhang
Keyword(s):  
State Space ◽  
Linear Time ◽  
Least Square ◽  
Internal Variables ◽  
Recursive Least Square ◽  
Unknown Parameters ◽  
Actual System ◽  
Highly Nonlinear ◽  
Space Modeling ◽  
Nonlinear State

The paper deals with nonlinear modeling and identification of an electrohydraulic control system for improving its tracking performance. We build the nonlinear state space model for analyzing the highly nonlinear system and then develop a Hammerstein-Wiener (H-W) model which consists of a static input nonlinear block with two-segment polynomial nonlinearities, a linear time-invariant dynamic block, and a static output nonlinear block with single polynomial nonlinearity to describe it. We simplify the H-W model into a linear-in-parameters structure by using the key term separation principle and then use a modified recursive least square method with iterative estimation of internal variables to identify all the unknown parameters simultaneously. It is found that the proposed H-W model approximates the actual system better than the independent Hammerstein, Wiener, and ARX models. The prediction error of the H-W model is about 13%, 54%, and 58% less than the Hammerstein, Wiener, and ARX models, respectively.

Kernel Recursive Least Square Approach for Power System Harmonic Estimation

2021 ◽  
pp. 1-16
Author(s):  
Omar Avalos ◽  
Erik Cuevas ◽  
Héctor G. Becerra ◽  
Jorge Gálvez ◽  
Salvador Hinojosa ◽  
...  
Keyword(s):  
Power System ◽  
Least Square ◽  
Recursive Least Square ◽  
Least Square Approach

scholarly journals An Effective Joint Soft-Sensing Strategy for Multi-Information under Diverse Vehicle Driving Scenarios

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 505
Author(s):  
Jianfeng Chen ◽  
Jiantian Sun ◽  
Shulin Hu ◽  
Yicai Ye ◽  
Haoqian Huang ◽  
...  
Keyword(s):  
Time Delay ◽  
Fast Response ◽  
Least Square ◽  
Accurate Information ◽  
Recursive Least Square ◽  
Soft Sensing ◽  
The Road ◽  
Vehicle Mass ◽  
Cubature Kalman Filter ◽  
Road Slope

A variety of accurate information inputs are of great importance for automotive control. In this paper, a novel joint soft-sensing strategy is proposed to obtain multi-information under diverse vehicle driving scenarios. This strategy is realized by an information interaction including three modules: vehicle state estimation, road slope observer and vehicle mass determination. In the first module, a variational Bayesian-based adaptive cubature Kalman filter is employed to estimate the vehicle states with the time-variant noise interference. Under the assumption of road continuity, a slope prediction model is proposed to reduce the time delay of the road slope observation. Meanwhile, a fast response nonlinear cubic observer is introduced to design the road slope module. On the basis of the vehicle states and road slope information, the vehicle mass is determined by a forgetting-factor recursive least square algorithm. In the experiments, a contrasted strategy is introduced to analyse and evaluate performance. Results declare that the proposed strategy is effective and has the advantages of low time delay, high accuracy and good stability.

A simple recursive least square algorithm of space–temporal joint equalizer

2012 ◽  
Vol 22 (6) ◽  
pp. 1145-1153 ◽  
Author(s):  
Maw-Lin Leou ◽  
Yi-Ching Liaw ◽  
Chien-Min Wu
Keyword(s):  
Least Square ◽  
Recursive Least Square ◽  
Recursive Least Square Algorithm
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