Dynamic Systems Enhanced by Electronic Circuits on 7D

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Abdulsattar Abdullah Hamad ◽  
M. Lellis Thivagar ◽  
Malik Bader Alazzam ◽  
Fawaz Alassery ◽  
Muayyad Mahmood Khalil ◽  
...  
Keyword(s):  
Health Care ◽  
Dynamic System ◽  
Dynamic Systems ◽  
Lyapunov Method ◽  
Control Unit ◽  
Linearization Method ◽  
Electronic Circuits ◽  
Linearization Methods ◽  
Mathematical System ◽  
Hybrid Synchronization

Hybrid synchronization is one of the most significant aspects of a dynamic system. We achieve nonlinear control unit results to synchronize two comparable 7D structures in this study. Many dynamic systems are directly connected to health care and directly enhance health. We employed linearization and Lyapunov as analytical methods, and since the linearization method does not need updating the Lyapunov function, it is more successful in achieving synchronization phenomena with better outcomes than the Lyapunov method. The two methods were combined, and the result was a striking resemblance to the dynamic system’s mistake. The mathematical system with control and error of the dynamic system was subjected to digital emulation. The digital good outcomes were comparable to the two methods previously stated. We compared the outcomes of three hybrid synchronizations based on Lyapunov and linearization methods. Finally, we used the existing system, presenting it in a new attractor and comparing the findings to those of other similar systems.

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.

Conditions for the local and global asymptotic stability of the time–fractional Degn–Harrison system

2020 ◽  
Vol 21 (7-8) ◽  
pp. 749-759
Author(s):  
Rachida Mezhoud ◽  
Khaled Saoudi ◽  
Abderrahmane Zaraï ◽  
Salem Abdelmalek
Keyword(s):  
Asymptotic Stability ◽  
Global Asymptotic Stability ◽  
Lyapunov Method ◽  
Sufficient Conditions ◽  
Reaction Diffusion ◽  
Linearization Method ◽  
Direct Lyapunov Method ◽  
Fractional Systems ◽  
Reaction Diffusion Model ◽  
Theoretical Results

AbstractFractional calculus has been shown to improve the dynamics of differential system models and provide a better understanding of their dynamics. This paper considers the time–fractional version of the Degn–Harrison reaction–diffusion model. Sufficient conditions are established for the local and global asymptotic stability of the model by means of invariant rectangles, the fundamental stability theory of fractional systems, the linearization method, and the direct Lyapunov method. Numerical simulation results are used to illustrate the theoretical results.

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.

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.

Hybrid Synchronization Between 5-th Order Hyperchaotic Chua Systems

2015 ◽  
Vol 13 (1-2) ◽  
pp. 25-34
Author(s):  
Dragomir Chantov
Keyword(s):  
Active Control ◽  
Secure Communication ◽  
Chaotic Systems ◽  
Lyapunov Method ◽  
Chaotic Synchronization ◽  
Synchronization System ◽  
Hybrid Synchronization ◽  
The Stability ◽  
Chaotic Secure Communication ◽  
Fifth Order

Abstract The aim of this paper is the design of a chaotic synchronization system with a special type of synchronization, called hybrid synchronization, on the basis of Chua’s fifth-order hyperchaotic model. When two chaotic systems are in hybrid synchronization, some of the systems’ variables pairs are in identical synchronization, and the rest are anti-synchronized. Hybrid synchronization schemes have advantages regarding the degree of signal protection, when they are used to build a chaotic secure communication system. The design of the system is accomplished by means of active control, where the Second Lyapunov method is used to prove the stability of the synchronization system.

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.

Linearization of Quadratic Drag to Estimate CALM Buoy Pitch Motion in Frequency-Domain and Experimental Validation

2009 ◽  
Author(s):  
Amir G. Salem ◽  
Sam Ryu ◽  
Arun S. Duggal ◽  
Raju V. Datla
Keyword(s):  
Frequency Domain ◽  
Diffraction Problem ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Linearization Method ◽  
Linear Wave ◽  
Period Range ◽  
Pitch Motion ◽  
Linearization Methods ◽  
Quadratic Drag

Estimate of the pitch motion of an oil offloading Catenary Anchor Leg Mooring (CALM) buoy is presented. Linearization of the quadratic drag/damping term is investigated by the frequency-domain analysis. The radiation problem is solved to estimate the added mass and radiation damping coefficients, and the diffraction problem is solved for the linear wave exciting loading. The equation of motion is solved by considering the linearized nonlinear drag/damping. The pitch motion response is evaluated at each wave frequency by iterative and various linearization methods of the nonlinear drag term. Comparisons between the linear and nonlinear damping effects are presented. Time-domain simulations of the buoy pitch motion were also compared with results from the frequency-domain analysis. Various linearization methods resulted in good estimate of the peak pitch response. However, only the stochastic linearization method shows a good agreement for the period range of the incident wave where typical pitch response estimate has not been correctly estimated.

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