Modelling and Systemic Analysis of Models of Dynamic Systems of Shaft Machining

2013 ◽  
Vol 282 ◽  
pp. 211-220 ◽  
Author(s):  
Antoni Świć ◽  
Jarosław Zubrzycki ◽  
Victor Taranenko
Keyword(s):  
Dynamic System ◽  
Hierarchical Structure ◽  
Dynamic Systems ◽  
Elastic Strain ◽  
Control Force ◽  
Plunge Grinding ◽  
Force Effect ◽  
Systemic Analysis

The specifics of modelling the dynamic system of turning as well as straight and plunge grinding of low rigidity shafts is presented in the paper. Methodology of developing models while machining shafts in elastic-deformable condition is shown. The specifics of processing of low rigidity elements is taken into account by introducing equations of constraint reflecting additional elastic strain in equation describing the control force effect. Systemic analysis of the developed models is performed and main hierarchical structure levels are given.

PERTURBATION PARAMETERS TUNING OF MULTI-OBJECTIVE OPTIMIZATION DIFFERENTIAL EVOLUTION AND ITS APPLICATION TO DYNAMIC SYSTEM MODELING

2015 ◽  
Vol 75 (11) ◽  
Author(s):  
Mohd Zakimi Zakaria ◽  
Hishamuddin Jamaluddin ◽  
Robiah Ahmad ◽  
Azmi Harun ◽  
Radhwan Hussin ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Dynamic System ◽  
Dynamic Systems ◽  
System Modeling ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Structure Selection ◽  
Dynamic System Modeling ◽  
Perturbation Parameters ◽  
Model Structure Selection

This paper presents perturbation parameters for tuning of multi-objective optimization differential evolution and its application to dynamic system modeling. The perturbation of the proposed algorithm was composed of crossover and mutation operators.  Initially, a set of parameter values was tuned vigorously by executing multiple runs of algorithm for each proposed parameter variation. A set of values for crossover and mutation rates were proposed in executing the algorithm for model structure selection in dynamic system modeling. The model structure selection was one of the procedures in the system identification technique. Most researchers focused on the problem in selecting the parsimony model as the best represented the dynamic systems. Therefore, this problem needed two objective functions to overcome it, i.e. minimum predictive error and model complexity.  One of the main problems in identification of dynamic systems is to select the minimal model from the huge possible models that need to be considered. Hence, the important concepts in selecting good and adequate model used in the proposed algorithm were elaborated, including the implementation of the algorithm for modeling dynamic systems. Besides, the results showed that multi-objective optimization differential evolution performed better with tuned perturbation parameters.

Parameter Design in Optimal Control Problems for Linear Dynamic Systems Using a Canonical Form

2013 ◽  
Vol 136 (1) ◽  
Author(s):  
Ui-Jin Jung ◽  
Gyung-Jin Park ◽  
Sunil K. Agrawal
Keyword(s):  
Optimal Control ◽  
Dynamic System ◽  
Dynamic Systems ◽  
Control Method ◽  
Linear Dynamic System ◽  
Parameter Design ◽  
Control Problems ◽  
State Transformation ◽  
Specific System ◽  
Linear Dynamic

Control problems in dynamic systems require an optimal selection of input trajectories and system parameters. In this paper, a novel procedure for optimization of a linear dynamic system is proposed that simultaneously solves the parameter design problem and the optimal control problem using a specific system state transformation. Also, the proposed procedure includes structural design constraints within the control system. A direct optimal control method is also examined to compare it with the proposed method. The limitations and advantages of both methods are discussed in terms of the number of states and inputs. Consequently, linear dynamic system examples are optimized under various constraints and the merits of the proposed method are examined.

scholarly journals FAULT DETECTION IN DISCRETE DYNAMIC SYSTEMS WITH UNCERTAINTY BASED ON INTERVAL OPTIMIZATION

2011 ◽  
Vol 16 (4) ◽  
pp. 549-557 ◽  
Author(s):  
Wei Li ◽  
Xiaoli Tian
Keyword(s):  
Fault Detection ◽  
Dynamic System ◽  
Dynamic Systems ◽  
Optimization Model ◽  
Uncertain Parameters ◽  
Interval Optimization ◽  
Numerical Examples ◽  
Discrete Dynamic ◽  
Discrete Dynamic System ◽  
Discrete Dynamic Systems

The imprecision and the uncertainty of many systems can be expressed with interval models. This paper presents a method for fault detection in uncertain discrete dynamic systems. First, the discrete dynamic system with uncertain parameters is formulated as an interval optimization model. In this model, we also assume that there are some errors of observation values of the inputs/outputs. Then, M. Hladík's newly proposed algorithm is exploited for this model. Some numerical examples are also included to illustrate the method efficiency.

Adding ingredients to the self-organizing dynamic system stew: Motivation, communication, and higher-level emotions – and don't forget the genes!

2005 ◽  
Vol 28 (2) ◽  
pp. 197-198 ◽  
Author(s):  
Ross Buck
Keyword(s):  
Dynamic System ◽  
Dynamic Systems ◽  
Social Cognitive ◽  
Moral Emotions ◽  
The Self ◽  
The Relationship ◽  
Self Organizing

Self-organizing dynamic systems (DS) modeling is appropriate to conceptualizing the relationship between emotion and cognition-appraisal. Indeed, DS modeling can be applied to encompass and integrate additional phenomena at levels lower than emotional interpretations (genes), at the same level (motives), and at higher levels (social, cognitive, and moral emotions). Also, communication is a phenomenon involved in dynamic system interactions at all levels.

Methods of chaos and self-organization in psychophysiology

10.12737/3394 ◽  
2014 ◽  
Vol 3 (1) ◽  
pp. 13-28
Author(s):  
Поскина ◽  
T. Poskina ◽  
Филатова ◽  
D. Filatova ◽  
Филатов ◽  
...  
Keyword(s):  
Phase Space ◽  
Dynamic System ◽  
Dynamic Systems ◽  
Self Organization ◽  
Deterministic Approach ◽  
System Behavior ◽  
System Parameters ◽  
A Value ◽  
Identification Theory

. All the H. Haken’s postulates (1970-2013) emphasize deterministic approach and level a value of trajectory of behavior of biological dynamic system in phase space of states. The significance of the latter theory is hard to overestimate, because according to phase space of states the new identification theory is being created and behavioral descriptions of biological dynamic systems are given. This new theory is based on measures of biological dynamic system parameters in phase space of states and does not need any concrete equations, it can be based on detection of quasi-attractors’ parameters of biological dynamic system behavior in phase space of states and characters are quasi-attractor parameters.

scholarly journals Orbital stability analysis of trajectories of highly nonlinear dynamic systems with feedback coupling

2021 ◽  
Vol 2131 (3) ◽  
pp. 032038
Author(s):  
G K Annakulova
Keyword(s):  
Dynamic System ◽  
Dynamic Systems ◽  
Limit Cycles ◽  
Nonlinear Dynamic ◽  
Phase Plane ◽  
Orbital Stability ◽  
Nonlinear Dynamic System ◽  
Phase Pattern ◽  
Highly Nonlinear ◽  
Complete Study

Abstract Orbital stability and qualitative study of the oscillations of a highly nonlinear dynamic system with feedback coupling are considered. For a highly nonlinear dynamic system with feedback coupling that satisfies Liénard’s theorem (on the existence and uniqueness of a periodic solution), a complete study of the phase pattern of the system is conducted. Applying the Poincaré criterion, the conditions for the existence of limit cycles and their Lyapunov stability are determined. The diagrams of phase trajectories are constructed numerically using the Mathcad 15 software package. Limit cycles are established, which are consistent with the limit cycles obtained by the Poincaré method. The behavior of trajectories outside the limit cycles is investigated. Recurrent homogeneous Pfaff equations are obtained, which determine the behavior of the systems “at infinity”. It was determined that the infinitely distant point of the horizontal axis is the only singular point for these equations. Linear approximations of recurrent homogeneous equations are obtained, which make it possible to determine the nature of the singular points. It was found that the trajectories then wind like a spiral on the limit cycles. Images of trajectories on the phase plane outside the limit cycles for the cases of degrees of nonlinearity under consideration are constructed.

scholarly journals A review of a motion model construction and simulation method for multi-level and multi-dimensional dynamic systems

2021 ◽  
Author(s):  
Guoxin Yang ◽  
Carl Yang
Keyword(s):  
Intellectual Property ◽  
Dynamic System ◽  
Dynamic Systems ◽  
Simulation Method ◽  
Model Construction ◽  
Motion Trajectory ◽  
Motion Model ◽  
Multi Level ◽  
Dynamic Subsystem ◽  
Dynamic Trajectory

On October 21, 2020, the invention “A multi-level and multi-dimensional dynamic system motion model construction and simulation method” was accepted by the State Intellectual Property Office of China. Through the construction and simulation of the motion model of a multi-level and multi-dimensional dynamic system, the motion trajectory of the motion factors in each dynamic system is nested and substituted into the next dynamic subsystem to establish the motion trajectory of the motion factors in its dynamic subsystem. Until the dynamic trajectory model of the motion factors in the minimum dynamic subsystem is obtained, the motion model of a multi-level and multi-dimensional dynamic system is constructed, which reveals the motion law that can unify the microscopic particles and all the motion factors in the macroscopic universe.

scholarly journals Antisystem-Approach (ASA) for Engineering of Wide Range of Dynamic Systems

2021 ◽  
Vol 3 (1) ◽  
pp. 52-66
Author(s):  
Serge Zacher
Keyword(s):  
Mathematical Model ◽  
Dynamic System ◽  
Dynamic Systems ◽  
Chemical Engineering ◽  
Original System ◽  
New Approach ◽  
Analysis And Design ◽  
Wide Range ◽  
Newton’S Laws ◽  
Definition Of

Following the famous third physical Newton’s laws, “for every action there is an equal and opposite re-action”, a new approach for analysis and design of dynamic systems was introduced by [Zacher, 1997] and called «Antisystem-Approach» (ASA). According to this approach, a single isolated dynamic system does not exist alone. For every dynamic system, which transfers its inputs into outputs with an operator A in one direction, there is an equal system with the same operator A, which transfers other inputs into outputs in opposite direction. The antisystem does not have to be a physical system; it can also be a mathematical model of the original system. The most important feature of ASA is the exact balance between a system and its antisystem, which is called “energy” or “intensity”. In the group theory the system and antisystem are denote as antisymmetric. They build duality, which is common in many branches of sciences as mathematics, physics, biology etc. In the twenty years since first publication of the ASA there were developed different methods and applications, which enable to simplify the engineering, analysing the antisystem instead of original system. In the proposed paper is given the definition of ASA und are shown its features. It is described, how the ASA was used in electrical and chemical engineering, automation, informatics. Only several applications will be discussed, although ASA-solutions are common and could be used for wide range of dynamic systems.

scholarly journals Some causal connections between stochastic dynamic systems

Filomat ◽  
2013 ◽  
Vol 27 (5) ◽  
pp. 865-873
Author(s):  
Ljiljana Petrovic
Keyword(s):  
Dynamic System ◽  
Hilbert Spaces ◽  
Dynamic Systems ◽  
Stochastic Dynamic ◽  
Realization Problem ◽  
Stochastic Dynamic System ◽  
Causal Connections ◽  
Causality Relationship ◽  
Stochastic Dynamic Systems

In this paper we consider a problem that follows directly from realization problem: how to find Markovian representations , even minimall, for a given family of Hilbert spaces, understood as outputs of a stochastic dynamic system S1, provided it is in a certain causality relationship with another family of Hilbert spaces , i. e. with some informations about states of a stochastic dynamic system S2. This paper is continuation of the papers Gill and Petrovic [7] and Petrovic [16,17].

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