A Robotics-Based Testbed for Verifying a Systematic Method of Identifying Contact-Dynamics Model Parameters

2007 ◽  
Author(s):  
Ou Ma ◽  
Jong H. Kim
Keyword(s):  
Point Contact ◽  
Contact Dynamics ◽  
System Level ◽  
Multiple Point ◽  
Model Parameters ◽  
Multiple Model ◽  
Dynamics Model ◽  
Systematic Method ◽  
Simulation Accuracy ◽  
Physical Test

Contact-dynamics simulations are increasingly being used in industry for enhancing product design and operation. Simulation accuracy depends not only on the simulation tools (i.e., formulation, algorithms, and computer codes) but also on the model parameters used in the simulation. Determination of contact-dynamics model parameters for a complex dynamic system is challenging. It is desirable to have a systematic method which can identify multiple model parameters directly from the physical test of the involved contact hardware. This paper describes a robotics-based experimental testbed which is specially designed for study and verification of a systematic method of identifying key parameters of multiple-point contact-dynamics models. The identification method can identify the stiffness, damping, and friction parameters all together from system-level hardware tests. The basic requirements of the testbed and how they are met by the design of the testbed are described. Some results of the preliminary test of the testbed obtained so far are also included.

A Systematic Method for Model Parameter Identification of Nonlinear Dynamics Systems Using Trajectory Pattern Method

2014 ◽  
Author(s):  
Jahangir Rastegar ◽  
Dake Feng
Keyword(s):  
Nonlinear Dynamics ◽  
System Dynamics ◽  
Parameter Identification ◽  
Model Parameters ◽  
Dynamics Model ◽  
Systematic Method ◽  
Model Parameter ◽  
Trajectory Pattern ◽  
Model Parameter Identification ◽  
Trajectory Pattern Method

In this paper, a new method is presented for model parameter identification of a large class of fully controlled nonlinear dynamics systems such as robot manipulators. The method uses trajectory patterns with feed-forward controls to identify model parameters of the system. The developed method ensures full system stability, does not require close initial estimated values for the parameters to be identified, and provides a systematic method of emphasizing on the estimation of the parameters associated with lower order terms of the system dynamics model and gradually upgrading the accuracy with which the model parameters, particularly those associated with the higher order terms of the system dynamics, are estimated. The developed method is based on Trajectory Pattern Method (TPM). In this method, for a pattern of motion the inverse dynamics model of the system is derived in algebraic form in terms of the trajectory pattern parameters. The structure of the feedback error with feedforward signal calculated with the estimated model parameters will then be fixed, and its measurement can be used to systematically upgrade the model parameter estimation. The mathematical proof of convergence of the developed method and results of its implementation on a robot manipulator with highly non-linear dynamics are provided.

A Systematic Method for Model Parameter Identification of Nonlinear Dynamics Systems Using Trajectory Pattern Method

2012 ◽  
Author(s):  
Jahangir Rastegar ◽  
Dake Feng
Keyword(s):  
Nonlinear Dynamics ◽  
System Dynamics ◽  
Parameter Identification ◽  
Model Parameters ◽  
Dynamics Model ◽  
Systematic Method ◽  
Model Parameter ◽  
Trajectory Pattern ◽  
Model Parameter Identification ◽  
Trajectory Pattern Method

In this paper, a new method is presented for model parameter identification of a large class of fully controlled nonlinear dynamics systems such as robot manipulators. The method uses trajectory patterns with feed-forward controls to identify model parameters of the system. The developed method ensures full system stability, does not require close initial estimated values for the parameters to be identified, and provides a systematic method of emphasizing on the estimation of the parameters associated with lower order terms of the system dynamics model and gradually upgrading the accuracy with which the model parameters, particularly those associated with the higher order terms of the system dynamics, are estimated. The developed method is based on Trajectory Pattern Method (TPM). In this method, for a pattern of motion the inverse dynamics model of the system is derived in algebraic form in terms of the trajectory pattern parameters. The structure of the feedback error with feedforward signal calculated with the estimated model parameters will then be fixed, and its measurement can be used to systematically upgrade the model parameter estimation. The mathematical proof of convergence of the developed method and results of its implementation on a robot manipulator with highly non-linear dynamics are provided.

scholarly journals Multiple point contact wear prediction and source identification scheme using a single channel blended airborne acoustic signature

2020 ◽  
Vol 53 (3) ◽  
pp. 283-288
Author(s):  
Muhammad Atayyab Shahid ◽  
Tariq Mairaj Khan ◽  
Kevin Lontin ◽  
Kanza Basit ◽  
Muhammad Khan
Keyword(s):  
Source Identification ◽  
Single Channel ◽  
Point Contact ◽  
Multiple Point ◽  
Wear Prediction ◽  
Identification Scheme ◽  
Contact Wear ◽  
Acoustic Signature

scholarly journals Application of Dynamically Constrained Interpolation Methodology in Simulating National-Scale Spatial Distribution of PM2.5 Concentrations in China

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 272
Author(s):  
Ning Li ◽  
Junli Xu ◽  
Xianqing Lv
Keyword(s):  
Transport Model ◽  
Ground Level ◽  
Kriging Interpolation ◽  
Model Parameters ◽  
National Scale ◽  
Constrained Interpolation ◽  
Simulation Accuracy ◽  
Interpolation Accuracy ◽  
Geographical Regions ◽  
The Right

Numerous studies have revealed that the sparse spatiotemporal distributions of ground-level PM2.5 measurements affect the accuracy of PM2.5 simulation, especially in large geographical regions. However, the high precision and stability of ground-level PM2.5 measurements make their role irreplaceable in PM2.5 simulations. This article applies a dynamically constrained interpolation methodology (DCIM) to evaluate sparse PM2.5 measurements captured at scattered monitoring sites for national-scale PM2.5 simulations and spatial distributions. The DCIM takes a PM2.5 transport model as a dynamic constraint and provides the characteristics of the spatiotemporal variations of key model parameters using the adjoint method to improve the accuracy of PM2.5 simulations. From the perspective of interpolation accuracy and effect, kriging interpolation and orthogonal polynomial fitting using Chebyshev basis functions (COPF), which have been proved to have high PM2.5 simulation accuracy, were adopted to make a comparative assessment of DCIM performance and accuracy. Results of the cross validation confirm the feasibility of the DCIM. A comparison between the final interpolated values and observations show that the DCIM is better for national-scale simulations than kriging or COPF. Furthermore, the DCIM presents smoother spatially interpolated distributions of the PM2.5 simulations with smaller simulation errors than the other two methods. Admittedly, the sparse PM2.5 measurements in a highly polluted region have a certain degree of influence on the interpolated distribution accuracy and rationality. To some extent, adding the right amount of observations can improve the effectiveness of the DCIM around existing monitoring sites. Compared with the kriging interpolation and COPF, the results show that the DCIM used in this study would be more helpful for providing reasonable information for monitoring PM2.5 pollution in China.

Co-Evolution of Communication and System Performance in Engineering Systems Design: A Stochastic Network-Behavior Dynamics Model

2021 ◽  
Author(s):  
Ashish M. Chaudhari ◽  
Erica L. Gralla ◽  
Zoe Szajnfarber ◽  
Jitesh H. Panchal
Keyword(s):  
System Design ◽  
System Performance ◽  
The Other ◽  
Model Parameters ◽  
Stochastic Network ◽  
Network Behavior ◽  
Dynamics Model ◽  
Behavior Dynamics ◽  
Technical Factors ◽  
Student Subjects

Abstract The socio-technical perspective on engineering system design emphasizes the mutual dynamics between interdisciplinary interactions and system design outcomes. How different disciplines interact with each other depends on technical factors such as design interdependence and system performance. On the other hand, the design outcomes are influenced by social factors such as the frequency of interactions and their distribution. Understanding this co-evolution can lead to not only better behavioral insights, but also efficient communication pathways. In this context, we investigate how to quantify the temporal influences of social and technical factors on interdisciplinary interactions and their influence on system performance. We present a stochastic network-behavior dynamics model that quantifies the design interdependence, discipline-specific interaction decisions, the evolution of system performance, as well as their mutual dynamics. We employ two datasets, one of student subjects designing an automotive engine and the other of NASA engineers designing a spacecraft. Then, we apply statistical Bayesian inference to estimate model parameters and compare insights across the two datasets. The results indicate that design interdependence and social network statistics both have strong positive effects on interdisciplinary interactions for the expert and student subjects alike. For the student subjects, an additional modulating effect of system performance on interactions is observed. Inversely, the total number of interactions, irrespective of their discipline-wise distribution, has a weak but statistically significant positive effect on system performance in both cases. However, excessive interactions mirrored with design interdependence and inflexible design space exploration reduce system performance. These insights support the case for open organizational boundaries as a way for increasing interactions and improving system performance.

Estimate the Performance of Multi-Model Estimation Algorithms

2013 ◽  
Vol 427-429 ◽  
pp. 1506-1509
Author(s):  
Yong Yan Yu
Keyword(s):  
Estimation Procedure ◽  
Model Parameters ◽  
Data Sets ◽  
Model Estimation ◽  
Multiple Model ◽  
Estimation Algorithms ◽  
Performance Metric ◽  
Testing Dataset ◽  
Correctness Criterion ◽  
Line Fitting

A robust estimation procedure is necessary to estimate the true model parameters in computer vision. Evaluating the multiple-model in the presence of outliers-robust is a fundamentally different task than the single-model problem.Despite there are many diversity multi-model estimation algorithms, it is difficult to pick an effective and advisably approach.So we present a novel quantitative evaluation of multi-model estimation algorithms, efficiency may be evaluated by either examining the asymptotic efficiency of the algorithms or by running them for a series of data sets of increasing size.Thus we create a specifical testing dataset,and introduce a performance metric, Strongest-Intersection.and using the model-aware correctness criterion. Finally, well show the validity of estimation strategy by the Experimention of line-fitting.

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