Experimental Verification of a Systematic Method for Identifying Contact-Dynamics Model Parameters

2007 ◽  
Author(s):  
Ou Ma ◽  
Jong Kim ◽  
Lucas Martinez
Keyword(s):  
Experimental Verification ◽  
Contact Dynamics ◽  
Model Parameters ◽  
Dynamics Model ◽  
Systematic Method

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.

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.

Stirling Thermocompressor: Lumped Parameter Modeling and Experimental Impact of Displacer Motion Profile on Work Output

2019 ◽  
Author(s):  
Seth Thomas ◽  
Eric J. Barth
Keyword(s):  
Experimental Validation ◽  
Model Parameters ◽  
Work Output ◽  
System Dynamics Model ◽  
Dynamics Model ◽  
Lumped Parameter ◽  
Stirling Engines ◽  
Simplified Modeling ◽  
Parameter Approach ◽  
Motion Profile

Abstract The thermocompressor, a little-known class of Stirling devices that efficiently compresses gas, presents new challenges for modeling and experimental validation. In modeling, traditional analytic assumptions about displacer motion are limiting. In experimental verification, few devices have actually been built and tested. In this paper, the authors test the feasibility of a lumped-parameter approach for predicting the performance of Stirling thermocompressors subject to different displacer motion profiles. Since the displacer of a thermocompressor can be controlled independently, unlike kinematic Stirling engines or dynamic Stirling engines, and has a large influence on output power and efficiency of the device, it is crucial that this is well captured by a system dynamics model for control. Key model parameters are simulated and results are experimentally verified on one of the few, if only, experimental thermocompressor platforms in the world. Conclusions are drawn regarding simplified modeling of the regenerator’s effectiveness and the effects on device work output by varying the displacer piston’s motion profile using different waveforms.

scholarly journals Understanding the Propagation and Control Strategies of Congestion in Urban Rail Transit Based on Epidemiological Dynamics Model

Information ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 258 ◽  
Author(s):  
Shi ◽  
Zhang ◽  
Lei
Keyword(s):  
Control Strategies ◽  
Urban Rail Transit ◽  
Model Parameters ◽  
Rail Transit ◽  
Dynamics Model ◽  
Passenger Flow ◽  
Urban Rail ◽  
Passenger Volume ◽  
And Control ◽  
Demand Control

With the construction of the urban rail transit (URT) network, the explosion of passenger volume is more rapid than the increased capacity of the newly built infrastructure, which results in serious passenger flow congestion (PLC). Understanding the propagation process of PLC is the key to formulate sustainable policies for reducing congestion and optimizing management. This study proposes a susceptible-infected-recovered (SIR) model based on the theories of epidemiological dynamics and complex network to analyze the PLC propagation. We simulate the PLC propagation under various situations, and analyze the sensitivity of PLC propagation to model parameters. Finally, the control strategies of restricting PLC propagation are introduced from two aspects, namely, supply control and demand control. The results indicate that both of the two control strategies contribute to relieving congestion pressure. The propagating scope of PLC is more sensitive when taking mild supply control, whereas, the demand control strategy shows some advantages in flexibly implementing and dealing with serious congestion. These results are of important guidance for URT agencies to understand the mechanism of PLC propagation and formulate appropriate congestion control strategies.

Steady State Characteristics of a Flapper-Nozzle Relief Valve

1987 ◽  
Vol 201 (2) ◽  
pp. 135-144 ◽  
Author(s):  
J S Yun ◽  
H S Cho
Keyword(s):  
Steady State ◽  
Pressure Control ◽  
Effective Area ◽  
Body Motion ◽  
Model Parameters ◽  
Dynamics Model ◽  
Relief Valve ◽  
Load Pressure ◽  
Hydraulic Load ◽  
The Relationship

The static and dynamic characteristics of flapper-nozzle type electromagnetic relief valves have not so far been investigated analytically in depth, although they have been widely used for hydraulic load pressure control. In this paper a non-linear model of the relief valve is formulated explicitly, based upon rigid-body motion and fluid dynamics. Model parameters such as discharge coefficients, effective area of the nozzle and the electromagnetic constant were identified from the steady state characteristics and physical dimensions of the valve. Based upon this constructed model the static characteristics such as the pressure override and the relationship between input current and main pressure were obtained analytically and compared with those obtained experimentally. The comparison shows that this constructed analytical model can precisely predict such characteristics.

Linearization and Model Reduction of Contact Dynamics Simulation

2011 ◽  
Author(s):  
Jianxun Liang ◽  
Ou Ma ◽  
Caishan Liu
Keyword(s):  
Model Reduction ◽  
Multibody Systems ◽  
Reduction Method ◽  
Contact Dynamics ◽  
Dynamics Simulation ◽  
Dynamics Model ◽  
Time Step ◽  
Reduction Techniques ◽  
Proposed Model ◽  
Simulation Time

Finite element methods are widely used for simulations of contact dynamics of flexible multibody systems. Such a simulation is computationally very inefficient because the system’s dimension is usually very large and the simulation time step has to be very small in order to ensure numerical stability. A potential solution to the problem is to apply a model reduction method in the simulation. Although many model reduction techniques have been developed, most of them cannot be readily applied due to the high nonlinearity of the involved contact dynamics model. This paper presents a solution to the problem. The approach is based on a modified Lyapunov balanced truncation method. A numerical example is presented to demonstrate that, by applying the proposed model reduction method, the simulation process can be significantly speeded up while the resulting error caused by the model reduction is still within an acceptable level.

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