Parameter identification for on-line model updating in hybrid simulations using a gradient-based method

2017 ◽  
Vol 47 (2) ◽  
pp. 269-293 ◽  
Author(s):  
Ming-Chieh Chuang ◽  
Shang-Hsien Hsieh ◽  
Keh-Chyuan Tsai ◽  
Chao-Hsien Li ◽  
Kung-Juin Wang ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Model Updating ◽  
Line Model ◽  
Gradient Based ◽  
On Line ◽  
Hybrid Simulations

Surface discontinuity detection using bacterial suspensions

2015 ◽  
Vol 59 (5) ◽  
pp. 723-730
Author(s):  
Telmo G. Santos ◽  
R. M. Miranda ◽  
F. Nascimento ◽  
Luísa Quintino ◽  
Pedro Vilaça ◽  
...  
Keyword(s):  
Discontinuity Detection ◽  
Surface Discontinuity

scholarly journals A Streamlined Search Algorithm for Path Modifications of a Safe Robot Manipulator*

2010 ◽  
Vol 1 (2) ◽  
Author(s):  
Nima Najmaei ◽  
Mehrdad R. Kermani
Keyword(s):  
Robot Manipulator ◽  
Search Algorithm ◽  
Search Space ◽  
Planning Problem ◽  
Minimum Number ◽  
Gradient Based ◽  
On Line ◽  
Line Path ◽  
Path Modification ◽  
Path Planning Problem

AbstractIn recent years, the interest in human-robot interactions has added a new dimension to the on-line path planning problem by requiring a method that guarantees a risk-free path. This paper presents a streamlined search algorithm for fast path modification. The algorithm is formulated as an optimization problem that evaluates alternative paths nearby each obstacle. Each path is evaluated based on the value of the danger assigned to that path. To reduce the size of the search space, the minimum number of via points necessary to alter the path is initially obtained using a geometrical method. Given the number of via points, the algorithm proceeds to locate the via points around the obstacle such that the resulting path through these via points satisfies all problem constraints. Obtaining a solution in this way renders a fast algorithm for path modification, while it better avoids problems often encountered in other gradient-based search algorithms. Case studies for two planar robots are provided to highlight some of the advantages of the proposed algorithm. Experimental results using a CRS-F3 robot manipulator validate the effectiveness of the algorithm for applications involving human-robot interactions.

On-Line Adaptation of Grid-Based Look-up Tables Using a Fast Linear Regression Technique

2004 ◽  
Vol 126 (4) ◽  
pp. 732-739 ◽  
Author(s):  
Michael Vogt ◽  
Norbert Mu¨ller ◽  
Rolf Isermann
Keyword(s):  
Linear Regression ◽  
Learning Algorithm ◽  
Feedforward Control ◽  
Spark Ignition Engine ◽  
Process Models ◽  
Convergence Time ◽  
Adaptation Algorithm ◽  
Gradient Based ◽  
On Line ◽  
Grid Based

Advanced control systems require accurate process models, while processes are often both nonlinear and time variant. After introducing the identification of nonlinear processes with grid-based look-up tables, a new learning algorithm for on-line adaptation of look-up tables is proposed. Using a linear regression approach, this new adaptation algorithm considerably reduces the convergence time in relation to conventional gradient-based adaptation algorithms. An application example and experimental results are shown for the learning feedforward control of the ignition angle of a spark ignition engine.

scholarly journals On-line blind separation of non-stationary signals

2005 ◽  
Vol 15 (1) ◽  
pp. 79-95
Author(s):  
Slavica Todorovic-Zarkula ◽  
Branimir Todorovic ◽  
Miomir Stankovic
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
State Space Model ◽  
Estimation Accuracy ◽  
Blind Separation ◽  
Space Model ◽  
Stationary Signals ◽  
Gradient Based ◽  
On Line ◽  
Source Signals

This paper addresses the problem of blind separation of non-stationary signals. We introduce an on-line separating algorithm for estimation of independent source signals using the assumption of non-stationary of sources. As a separating model, we apply a self-organizing neural network with lateral connections, and define a contrast function based on correlation of the network outputs. A separating algorithm for adaptation of the network weights is derived using the state-space model of the network dynamics, and the extended Kalman filter. Simulation results obtained in blind separation of artificial and real-world signals from their artificial mixtures have shown that separating algorithm based on the extended Kalman filter outperforms stochastic gradient based algorithm both in convergence speed and estimation accuracy.

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.

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