Decoupling joint and elastic accelerations in deformable mechanical systems using non-linear recursive formulations

2007 ◽  
Vol 73 (2) ◽  
pp. 273-295 ◽  
Author(s):  
Yunn-Lin Hwang
Keyword(s):  
Mechanical Systems ◽  
Non Linear

Non-linear Dynamics of Structures and Mechanical Systems

2006 ◽  
Vol 84 (24-25) ◽  
pp. 1561-1564
Author(s):  
P. Ribeiro ◽  
B.H.V. Topping ◽  
C.A. Mota Soares
Keyword(s):  
Mechanical Systems ◽  
Linear Dynamics ◽  
Non Linear

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.

Decentralised robust control of interconnected uncertain non-linear mechanical systems

2010 ◽  
Vol 8 (1/2/3/4) ◽  
pp. 114
Author(s):  
Zi Jiang Yang ◽  
Youichirou Fukushima ◽  
Shunshoku Kanae ◽  
Kiyoshi Wada
Keyword(s):  
Robust Control ◽  
Mechanical Systems ◽  
Non Linear

Non-linear Control for Underactuated Mechanical Systems

2002 ◽  
Vol 55 (4) ◽  
pp. B67 ◽  
Author(s):  
I Fantoni ◽  
R Lozano ◽  
SC Sinha
Keyword(s):  
Mechanical Systems ◽  
Linear Control ◽  
Underactuated Mechanical Systems ◽  
Non Linear

Non-linear optimal fuzzy control synthesis for robust output tracking of uncertain micro-electro-mechanical systems

2016 ◽  
Vol 39 (8) ◽  
pp. 1146-1160 ◽  
Author(s):  
Alireza Modirrousta ◽  
Mostafa Shokrian Zeini ◽  
Tahereh Binazadeh
Keyword(s):  
Fuzzy Control ◽  
Sliding Mode ◽  
Reference Signal ◽  
Mechanical Systems ◽  
Control Synthesis ◽  
Sliding Mode Controller ◽  
Micro Electro Mechanical Systems ◽  
Output Tracking ◽  
Non Linear ◽  
Variable Universe Fuzzy Control

This paper considers the output tracking problem for micro-electro-mechanical systems (MEMS) under uncertainties and external disturbances. The robust non-linear controllers are designed by two methods. The first method consists of a backstepping strategy combined with a first-order sliding mode controller. Also, in order to reduce the chattering effect and to improve the robustness of the proposed scheme, a new variable universe fuzzy control action with an adaptive coefficient is used instead of the signum function in the switching control law. In the proposed fuzzy scheme, the centres of the output membership functions are optimized via three heuristic optimization algorithms including the artificial bee colony (ABC) algorithm, ant colony optimization (ACO) and particle swarm optimization (PSO). In the second method, a class of second-order sliding mode controller is combined with the backstepping strategy. The second controller includes the proposed optimal fuzzy controllers of the first method. The stability of the closed-loop systems in both approaches are proved via the Lyapunov stability criterion and the conditions of stabilization are provided by linear matrix inequalities (LMIs). Numerical simulations are carried out to verify the theoretical results and to demonstrate the robust performance of the proposed controller in output tracking of the time-varying reference signal.

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