A Method for Identifying Parameters of Mechanical Joints

1990 ◽  
Vol 57 (2) ◽  
pp. 337-342 ◽  
Author(s):  
J. Wang ◽  
P. Sas
Keyword(s):  
Physical Parameter ◽  
Mechanical Systems ◽  
Degree Of Freedom ◽  
Modal Testing ◽  
Physical Parameters ◽  
Mechanical Joints ◽  
Joint Parameters

A method for identifying the physical parameters of joints in mechanical systems is presented. In the method, a multi-d.o.f. (degree-of-freedom) system is transformed into several single d.o.f. systems using selected eigenvectors. With the result from modal testing, each single d.o.f. system is used to solve for a pair of unknown physical parameters. For complicated cases where the exact eigenvector cannot be obtained, it will be proven that a particular physical parameter has a stationary value in the neighborhood of an eigenvector. Therefore, a good approximation for a joint physical parameter can be obtained by using an approximate eigenvector and the exact value for the joint parameters can be reached by carrying out this process in an iterative way.

Updating Finite Element Model of Combined Structures on the Basis of Dynamic Test Results

1996 ◽  
Author(s):  
Chen Xin ◽  
Qin Ye ◽  
Yuan Xiguang ◽  
Zhang Ping ◽  
Sun Jian
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Test Data ◽  
Dynamic Test ◽  
Element Model ◽  
Physical Parameters ◽  
Structural Joint ◽  
Combined Structure ◽  
Joint Parameters ◽  
Two Stages

Abstract According to the real situation, a new method of updating the finite element model (FEM) of a combined structure step by step is proposed in this paper. It is assumed that there are two types of error when establishing the FEMs. One of them results from the simplifications, in fact, it is severe for complicated structures, which usually assume many simplifications; the other is from the process of identifying structural joint parameters. For this reason, it is recommended that the FEM should be established in two stages. At the first stage, the local physical parameters relating with the simplifications are corrected by using the dynamic test data of the corresponding substructures. Then, the structural joint parameters that link the substructures are corrected by the dynamic test data of the combined structure as a whole. The updating formula is presented and proved, and its algorithm is also described. And the experimental results show that the efficiency and accuracy of the proposed method are quite satisfactory.

Identification of Physical Helicopter Models Using Subspace Identification

2020 ◽  
Vol 65 (2) ◽  
pp. 1-14
Author(s):  
Sevil Avcıoğlu ◽  
Ali Türker Kutay ◽  
Kemal Leblebicioğlu
Keyword(s):  
Parameter Estimation ◽  
State Space ◽  
Nonlinear Optimization ◽  
Physical Parameter ◽  
Test Data ◽  
Flight Test ◽  
The State ◽  
Physical Parameters ◽  
Subspace Identification ◽  
Study Results

Subspace identification is a powerful tool due to its well-understood techniques based on linear algebra (orthogonal projections and intersections of subspaces) and numerical methods like singular value decomposition. However, the state space model matrices, which are obtained from conventional subspace identification algorithms, are not necessarily associated with the physical states. This can be an important deficiency when physical parameter estimation is essential. This holds for the area of helicopter flight dynamics, where physical parameter estimation is mainly conducted for mathematical model improvement, aerodynamic parameter validation, and flight controller tuning. The main objective of this study is to obtain helicopter physical parameters from subspace identification results. To achieve this objective, the subspace identification algorithm is implemented for a multirole combat helicopter using both FLIGHTLAB simulation and real flight-test data. After obtaining state space matrices via subspace identification, constrained nonlinear optimization methodologies are utilized for extracting the physical parameters. The state space matrices are transformed into equivalent physical forms via the "sequential quadratic programming" nonlinear optimization algorithm. The required objective function is generated by summing the square of similarity transformation equations. The constraints are selected with physical insight. Many runs are conducted for randomly selected initial conditions. It can be concluded that all of the significant parameters can be obtained with a high level of accuracy for the data obtained from the linear model. This strongly supports the idea behind this study. Results for the data obtained from the nonlinear model are also evaluated to be satisfactory in the light of statistical error analysis. Results for the real flight-test data are also evaluated to be good for the helicopter modes that are properly excited in the flight tests.

Modal Control of Underactuated Systems

2010 ◽  
Author(s):  
D. Dane Quinn ◽  
Vineel Mallela
Keyword(s):  
Control System ◽  
Mechanical System ◽  
Mechanical Systems ◽  
Degree Of Freedom ◽  
Feedback Gain ◽  
Underactuated Systems ◽  
Modal Control ◽  
Underactuated Mechanical Systems ◽  
Sensors And Actuators ◽  
Sensor Actuator

This work addresses the modal control of underactuated mechanical systems, whereby the number of actuators is less than the degree-of-freedom of the underlying mechanical system. The performance of the control system depends on the structure of the feedback gain matrix, that is, the coupling between sensors and actuators. This coupling is often not arbitrary, but the topology of the sensor-actuator network can be a fixed constraint of the control system. This work examines the influence of this structure on the performance of the overlying control system.

scholarly journals How to Make the Physical Parameters Small

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Sergey G. Rubin
Keyword(s):  
Physical Parameter ◽  
Extra Dimensions ◽  
Energy Levels ◽  
The Other ◽  
Physical Parameters ◽  
Green Functions ◽  
Other Hand ◽  
Low Energies ◽  
Parameter Values ◽  
Positive Point

We study the evolution of the physical parameter values defined at the sub-planckian energies to values at low energies. The Wilson action is the basis of the research. The presence of the compact extra dimensions has two consequences. The positive point is that the integration over extra dimensions is a promising way to substantially reduce the parameters to be comparable with the observational values. On the other hand, the discreteness of the energy levels of compact extra dimensions complicates the analysis. This difficulty can be overcome with the truncated Green functions.

Technical Note: A two-degree-of-freedom ambulance stretcher suspension: Part 3: Laboratory and road test performance

1998 ◽  
Vol 212 (5) ◽  
pp. 401-407 ◽  
Author(s):  
R. J. Henderson ◽  
J. K. Raine
Keyword(s):  
Test Performance ◽  
Natural Frequencies ◽  
Low Cost ◽  
Mechanical Systems ◽  
Technical Note ◽  
Suspension System ◽  
Degree Of Freedom ◽  
Road Test ◽  
Pneumatic Suspension ◽  
Two Degree Of Freedom

Parts 1 and 2 of this paper gave a design overview and described the dynamics of a prototype two-degree-of-freedom pneumatic suspension for an ambulance stretcher. This concluding part briefly reviews laboratory shaker table and ambulance road test performance of the suspension with passive pneumatic damping. The suspension system is found to offer compact low-cost isolation with lower natural frequencies than achieved in earlier mechanical systems.

Inertia Matrix Singularity of Series-Chain Spatial Manipulators With Point Masses

1993 ◽  
Vol 115 (4) ◽  
pp. 723-725 ◽  
Author(s):  
Sunil K. Agrawal
Keyword(s):  
Computer Simulation ◽  
Dynamic Behavior ◽  
Mechanical Systems ◽  
Positive Definite ◽  
Degree Of Freedom ◽  
Numerical Errors ◽  
Singular Configurations ◽  
Spatial Series ◽  
Inertia Matrix ◽  
Incomplete Models

Often, the dynamic behavior of multi-degree-of-freedom mechanical systems such as robots and manipulators is studied by computer simulation. An important step in this simulation is the inversion of inertia matrix of the system. In singular configurations of the inertia matrix, the simulation is prone to large numerical errors. Usually, it is believed that an inertia matrix is always positive definite. In this paper, it is shown that for spatial series-chain manipulators, when the links are modeled as point masses, a multitude of configurations exists when the inertia matrix becomes singular. These singularities arise because point masses lead to incomplete models of the system.

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