State-space representation and optimal control of non-linear material deformation using the finite element method

1993 ◽  
Vol 36 (12) ◽  
pp. 1967-1986 ◽  
Author(s):  
Ramana V. Grandhi ◽  
Anand Kumar ◽  
Anil Chaudhary ◽  
James C. Malas
Keyword(s):  
Finite Element Method ◽  
Optimal Control ◽  
Finite Element ◽  
State Space ◽  
Space Representation ◽  
The Finite Element Method ◽  
State Space Representation ◽  
Material Deformation ◽  
Non Linear ◽  
Linear Material

Model-Based Optimal Control of a Small Remotely Operated Vehicle

2017 ◽  
Author(s):  
Andrés Felipe Aldana Afanador ◽  
Helio Sneyder Esteban Villegas ◽  
Sebastián Roa Prada
Keyword(s):  
Optimal Control ◽  
Finite Element ◽  
Control System ◽  
Parameter Identification ◽  
State Space ◽  
Control Strategy ◽  
Space Representation ◽  
Underactuated System ◽  
Remotely Operated Vehicle ◽  
State Space Representation

Remotely Operated Vehicles, ROVs, are useful devices that can greatly assist humans in the development deep-sea exploration and underwater tasks. These unmanned vehicles require human intervention for the realization of its underwater jobs and have the ability to carry multiple instruments, sensors and actuators according to the application. There are numerous commercial ROV platforms, ranging from inexpensive to very advanced and costly systems. Being an underactuated system, one of the most important factors in the development of a ROV is the design of its control system. Depending on the quality of the implemented control strategy, the functioning of the vehicle may or may not fail, or the accuracy with which its assignments are done may be seriously compromised. Different control strategies can be utilized for the stabilization and maneuvering of a ROV. The design of these strategies often require system parameter identification. Appropriate modeling and knowledge about the dynamic behavior of the system is essential for a successful parameter identification. One of the main parameters that must be identified is the drag coefficient of the ROV as it moves in the fluid. This parameter can be either experimentally measured, or estimated using the finite element method, to quantify the forces due to fluid-structure interaction. This work seeks the design and comparison of different advanced control techniques as applied to a small ROV. A commercial small ROV system has been chosen as the object of study and finite element simulations were carried out to estimate some of its mechanic parameters, using the commercial software COMSOL Multiphysics®. The nonlinear model of the system is developed and linearized to obtain its state space representation. The state space representation of the system is then used in the design of a LQR control system. The comparisons of the responses of the compensated systems allows assessing the suitability of the optimal control strategy for stabilization of ROVs.

Combining regression trees and the finite element method to define stress models of highly non-linear mechanical systems

2009 ◽  
Vol 44 (6) ◽  
pp. 491-502 ◽  
Author(s):  
R Lostado ◽  
F J Martínez-De-Pisón ◽  
A Pernía ◽  
F Alba ◽  
J Blanco
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Mechanical Systems ◽  
Regression Trees ◽  
Support Vector ◽  
The Finite Element Method ◽  
Multiple Contacts ◽  
Non Linear ◽  
Instance Space ◽  
Element Method

This paper demonstrates that combining regression trees with the finite element method (FEM) may be a good strategy for modelling highly non-linear mechanical systems. Regression trees make it possible to model FEM-based non-linear maps for fields of stresses, velocities, temperatures, etc., more simply and effectively than other techniques more widely used at present, such as artificial neural networks (ANNs), support vector machines (SVMs), regression techniques, etc. These techniques, taken from Machine Learning, divide the instance space and generate trees formed by submodels, each adjusted to one of the data groups obtained from that division. This local adjustment allows good models to be developed when the data are very heterogeneous, the density is very irregular, and the number of examples is limited. As a practical example, the results obtained by applying these techniques to the analysis of a vehicle axle, which includes a preloaded bearing and a wheel, with multiple contacts between components, are shown. Using the data obtained with FEM simulations, a regression model is generated that makes it possible to predict the contact pressures at any point on the axle and for any condition of load on the wheel, preload on the bearing, or coefficient of friction. The final results are compared with other classical linear and non-linear model techniques.

scholarly journals Non-linear analysis and warping of tubular pipe conveyors by the finite element method

2007 ◽  
Vol 46 (1-2) ◽  
pp. 95-108 ◽  
Author(s):  
J.J. del Coz Díaz ◽  
P.J. García Nieto ◽  
J.A. Vilán Vilán ◽  
A. Martín Rodríguez ◽  
J.R. Prado Tamargo ◽  
...  
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Linear Analysis ◽  
The Finite Element Method ◽  
Non Linear ◽  
Element Method
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