scholarly journals Recursive Least Squares Filtering Algorithms for On-Line Viscoelastic Characterization of Biosamples

Actuators ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 74 ◽  
Author(s):  
Paolo Di Giamberardino ◽  
Maria Aceto ◽  
Oliviero Giannini ◽  
Matteo Verotti
Keyword(s):  
Least Square ◽  
Constitutive Law ◽  
Recursive Least Squares ◽  
Measurement Tool ◽  
Recursive Least Square ◽  
Novel Approach ◽  
Rigid Body Model ◽  
Gradient Based ◽  
On Line

The mechanical characterization of biological samples is a fundamental issue in biology and related fields, such as tissue and cell mechanics, regenerative medicine and diagnosis of diseases. In this paper, a novel approach for the identification of the stiffness and damping coefficients of biosamples is introduced. According to the proposed method, a MEMS-based microgripper in operational condition is used as a measurement tool. The mechanical model describing the dynamics of the gripper-sample system considers the pseudo-rigid body model for the microgripper, and the Kelvin–Voigt constitutive law of viscoelasticity for the sample. Then, two algorithms based on recursive least square (RLS) methods are implemented for the estimation of the mechanical coefficients, that are the forgetting factor based RLS and the normalised gradient based RLS algorithms. Numerical simulations are performed to verify the effectiveness of the proposed approach. Results confirm the feasibility of the method that enables the ability to perform simultaneously two tasks: sample manipulation and parameters identification.

Auto-Tuning Digital Control Based on Parameters Identification and Disturbance Observer

2006 ◽  
Vol 505-507 ◽  
pp. 529-534 ◽  
Author(s):  
Chin Sheng Chen ◽  
Yu Reng Lee
Keyword(s):  
Controller Design ◽  
Tracking Error ◽  
Parameters Identification ◽  
Least Square ◽  
Servo Motor ◽  
Recursive Least Square ◽  
Estimated Parameters ◽  
On Line ◽  
Feedforward Controller ◽  
Auto Tuning

This paper presented a digital servo driver that realizes an auto-tuning feedback and feedforward controller design using on-line parameters identification. Firstly, the variant inertia constant, damping constant and the disturbed load torque of the servo motor are estimated by the recursive least square (RLS) estimator, which is composed of an RLS estimator and a disturbance torque compensator. Furthermore, the auto-tuning algorithm of feedback and feedforward controller is realized according to the estimated parameters to match the tracking specification. The proposed auto-tuning digital servo controllers are evaluated and compared experimentally with a traditional controller on a microcomputer-controlled servo motor positioning system. The experimental results show that this auto-tuning digital servo system remarkably reduces the tracking error.

Adaptive Order Tracking Technique Using Recursive Least-Square Algorithm

2002 ◽  
Vol 124 (4) ◽  
pp. 502-511 ◽  
Author(s):  
Mingsian R. Bai ◽  
Jihjau Jeng ◽  
Chingyu Chen
Keyword(s):  
Least Square ◽  
Recursive Least Squares ◽  
Frequency Resolution ◽  
Shaft Speed ◽  
Recursive Least Square ◽  
Rls Algorithm ◽  
Tracking Technique ◽  
Order Tracking ◽  
Conventional Methods ◽  
Bandpass Signals

Order tracking technique is one of the important tools for diagnosis of rotating machinery. Conventional methods of order tracking are primarily based on Fourier analysis with reference to shaft speed. Resampling is generally required in the fast Fourier transform (FFT)-based methods to compromise between time and frequency resolution for varying shaft speeds. Conventional methods suffer from a number of shortcomings. In particular, smearing problems arise when closely spaced orders or crossing orders are present. Conventional methods also are ineffective for the applications involving multiple independent shaft speeds. This paper presents an adaptive order tracking technique based on the Recursive Least-Squares (RLS) algorithm to overcome the problems encountered in conventional methods. In the proposed method, the problem is treated as the tracking of frequency-varying bandpass signals. Order amplitudes can be calculated with high resolution by using the proposed method in real-time fashion. The RLS order tracking technique is applicable whether it is a single-axle or multi-axle system.

INVERSE DYNAMIC INPUT ESTIMATION OF A SEISMIC SOIL–STRUCTURE INTERACTION SYSTEM

2014 ◽  
Vol 06 (04) ◽  
pp. 1450040 ◽  
Author(s):  
MING-HUI LEE
Keyword(s):  
Soil Structure ◽  
Estimation Method ◽  
Least Square ◽  
Recursive Least Squares ◽  
Soil Structure Interaction ◽  
Recursive Least Square ◽  
Inverse Dynamic ◽  
Input Estimation ◽  
Structure Interaction ◽  
Interaction System

The innovative fuzzy weighting input estimation method is used to estimate the ground motion acceleration of a soil–structure interaction system in this study. The input estimation method is comprised of the Kalman filter without the input term and the fuzzy weighting recursive least square estimator. The recursive least squares estimator (RLSE) is weighted using the fuzzy weighting factor. The superior capabilities of this inverse method are demonstrated by solving the soil–structure interaction estimation problem. The precision of the proposed method is verified using the actual earthquake acceleration. The results show that this method has the advantages of stability and accuracy.

On-line training and pruning for recursive least square algorithms

1996 ◽  
Vol 32 (23) ◽  
pp. 2152 ◽  
Author(s):  
Chi Sing Leung ◽  
Kwok Wo Wong ◽  
Pui Fai Sum ◽  
Lai Wan Chan
Keyword(s):  
Least Square ◽  
Recursive Least Square ◽  
On Line ◽  
Training And Pruning

scholarly journals Parameter Identification of BLDC Motor Using Electromechanical Tests and Recursive Least-Squares Algorithm: Experimental Validation

Actuators ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 143
Author(s):  
Jose Jimenez-Gonzalez ◽  
Felipe Gonzalez-Montañez ◽  
Victor Manuel Jimenez-Mondragon ◽  
Jesús Ulises Liceaga-Castro ◽  
Rafael Escarela-Perez ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Real Time ◽  
Digital Simulation ◽  
Least Square ◽  
Recursive Least Squares ◽  
Rotor Speed ◽  
Recursive Least Square ◽  
Brushless Dc ◽  
Speed Controller ◽  
Two Stages

In this article, the parameter identification of a brushless DC motor (BLDC) is presented. The approach here presented is based on a direct identification considering a three-phase line-to-line voltage electromagnetic torque as function of the electric currents and rotor speed. The estimation is divided into two stages. First, the electrical parameters are estimated by well-known no-load and DC tests. Consequently, estimation of mechanical parameters is performed using a recursive Least Square Algorithm. The proposed approach is validated by comparing model responses to motor real time responses. Additionally, the design, digital simulation and real time implementation of a PI rotor speed controller, based on the estimated model, validate the identification proposal presented here.

Direct Adaptive Fuzzy Predictive Control and its Application to CSTR Process

2012 ◽  
Vol 241-244 ◽  
pp. 1191-1194
Author(s):  
Cheng Li Su ◽  
Ma Lina ◽  
Ping Li
Keyword(s):  
Predictive Control ◽  
Fuzzy Model ◽  
Least Square ◽  
Effective Control ◽  
Continuous Stirred Tank Reactor ◽  
Recursive Least Square ◽  
On Line ◽  
S Model ◽  
Takagi Sugeno ◽  
Fuzzy Predictive Control

In order to obtain accurate prediction model and avoid solving nonlinear programming problem, a direct adaptive predictive control (DAPC) method is proposed. Firstly, a nonlinear system was described based on Takagi-Sugeno (T-S) fuzzy models. Assuming that that the antecedent parameters of T-S models were kept, the consequent parameters were identified on-line by using the weighted recursive least square (WRLS) method. Secondly, the identified parameters of fuzzy model were used to directly receive the model predicted output with direct iterative for the T-S model. Finally, the application results for continuous stirred tank reactor (CSTR) process show that the proposed algorithm is an effective control strategy with excellent tracing ability. The proposed algorithm is a good way to resolve the two major problems, modeling and optimization, and provides a guarantee for high-precision control of nonlinear systems.

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