Integrated System for Soft Tissue Dynamic Simulation

2010 ◽  
Author(s):  
Xiaodong Zhao ◽  
Baoxiang Shan ◽  
Assimina A. Pelegri
Keyword(s):  
Finite Element ◽  
Soft Tissue ◽  
State Space ◽  
Soft Tissues ◽  
Mathematical Formulation ◽  
State Space Model ◽  
Order Reduction ◽  
Integrated System ◽  
Space Model ◽  
Tissue Models

An integrated system is built to model and simulate the dynamic response of soft tissues. The mathematical formulation employs finite element and model order reduction approaches to develop a state space model for soft tissues that allows for time-efficient numerical analysis. The stimulus device and signal processing routines are built in Matlab/Simulink and then integrated with the finite element state space model. This integrated system facilitates expeditious numerical evaluation of different soft tissue models subjected to dynamic excitation. It further elucidates the effect of different stimulus sources, as well as relative influences of different sources of uncertainty.

scholarly journals Piezoelectric Beam Finite Element Model and Its Reduction and Control

2021 ◽  
Vol 71 (1) ◽  
pp. 87-106
Author(s):  
Kutiš Vladimír ◽  
Paulech Juraj ◽  
Gálik Gálik ◽  
Murín Justín
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
State Space ◽  
State Space Model ◽  
Element Model ◽  
Functionally Graded ◽  
Fem Model ◽  
Space Model ◽  
Piezoelectric Beam ◽  
Reduced State

Abstract The paper deals with the development of the finite element method (FEM) model of piezoelectric beam elements, where the piezoelectric layers are located on the outer surfaces of the beam core, which is made of functionally graded material. The created FEM model of piezoelectric beam structure is reduced using the modal truncation method, which is one of model order reduction (MOR) method. The results obtain from reduced state-space model are compared with results obtain from finite element model. MOR state-space model is also used in the design of the linear quadratic regulator (LQR). Created reduced state-space model with feedback with the LQR controller is analysed and compared with the results from FEM model.

No-load induction motor core losses using a combined finite-element/state-space model

1992 ◽  
Vol 28 (5) ◽  
pp. 2820-2822 ◽  
Author(s):  
M.K. Jamil ◽  
P. Baldassari ◽  
N.A. Demerdash
Keyword(s):  
Finite Element ◽  
State Space ◽  
Induction Motor ◽  
State Space Model ◽  
Space Model ◽  
Core Losses

Design of Robust Vibration Controller for a Smart Panel Using Finite Element Model

2002 ◽  
Vol 124 (2) ◽  
pp. 265-276 ◽  
Author(s):  
W. Chang ◽  
Senthil V. Gopinathan ◽  
V. V. Varadan ◽  
V. K. Varadan
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Model Reduction ◽  
State Space ◽  
State Space Model ◽  
Element Model ◽  
Robust Controller ◽  
Space Model ◽  
Smart Panel ◽  
Low Order

This paper presents a model reduction method and uncertainty modeling for the design of a low-order H∞ robust controller for suppression of smart panel vibration. A smart panel with collocated piezoceramic actuators and sensors is modeled using solid, transition, and shell finite elements, and then the size of the model is reduced in the state space domain. A robust controller is designed not only to minimize the panel vibration excited by applied uniform acoustic pressure, but also to be reliable in real world applications. This paper introduces the idea of Modal Hankel Singular values (MHSV) to reduce the finite element model to a low-order state space model with minimum model reduction error. MHSV measures balanced controllability and observability of each resonance mode to deselect insignificant resonance modes. State space modeling of realistic control conditions are formulated in terms of uncertainty variables. These uncertainty variables include uncertainty in actuators and sensors performances, uncertainty in the knowledge of resonance frequencies of the structure, damping ratio, static stiffness, unmodeled high resonance vibration modes, etc. The simplified model and the uncertainty model are combined as an integrated state space model, and then implemented in the H∞ control theory for controller parameterization. The low-order robust controller is easy to implement in an analog circuit to provide a low cost solution in a variety of applications where cost may be a limiting factor.

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