Synchronization, Non-linear Dynamics and Control

2018 ◽  
Author(s):  
Ginestra Bianconi
Keyword(s):  
Multivariate Time Series ◽  
Network Control ◽  
Structural Constraints ◽  
Multilayer Networks ◽  
Linear Dynamics ◽  
Multiplex Network ◽  
Non Linear ◽  
Dynamics And Control ◽  
The Stability ◽  
And Control

This chapter is entirely devoted to characterizing non-linear dynamics on multilayer networks. Special attention is given to recent results on the stability of synchronization that extend the Master Stability Function approach to the multilayer networks scenario. Discontinous synchronization transitions on multiplex networks recently reported in the literature are also discussed, and their application discussed in the context of brain networks. This chapter also presents an overview of the major results regarding pattern formation in multilayer networks, and the proposed characterization of multivariate time series using multiplex visibility graphs. Finally, the chapter discusses several approaches for multiplex network control where the dynamical state of a multiplex network needs to be controlled by eternal signals placed on replica nodes satisfying some structural constraints.

Neural network control design for a rigid-link electrically driven robot

2003 ◽  
Vol 217 (2) ◽  
pp. 99-107 ◽  
Author(s):  
S N Huang ◽  
K K Tan ◽  
T H Lee
Keyword(s):  
Neural Network ◽  
Control Design ◽  
Network Control ◽  
Second Step ◽  
Neural Network Control ◽  
Linear Dynamics ◽  
Rigid Link ◽  
Non Linear ◽  
Robot Systems ◽  
The Stability

In this paper, a back-stepping scheme for rigid-link electrically driven (RLED) robot systems is proposed. A two-step controller is presented: the first step is a virtual controller, while the second step is an actual one. A neural network is used to approximate the unknown non-linear dynamics in the system. The stability can be guaranteed by using a rigid proof. A simulation is used to illustrate the effectiveness of the proposed algorithm.

Simple System Dynamics and Control System Project Models

2014 ◽  
pp. 113-133
Author(s):  
A. S. White
Keyword(s):  
Control System ◽  
Software Project ◽  
Software Projects ◽  
Initial State ◽  
Control Models ◽  
Non Linear ◽  
Dynamics And Control ◽  
Control System Model ◽  
System Project ◽  
And Control

This chapter examines the established Systems Dynamics (SD) methods applied to software projects in order to simplify them. These methods are highly non-linear and contain large numbers of variables and built-in decisions. A SIMULINK version of an SD model is used here and conclusions are made with respect to the initial main controlling factors, compared to a NASA project. Control System methods are used to evaluate the critical features of the SD models. The eigenvalues of the linearised system indicate that the important factors are the hiring delay time, the assimilation time, and the employment time. This illustrates how the initial state of the system is at best neutrally stable with control only being achieved with complex non-linear decisions. The purpose is to compare the simplest SD and control models available required for “good” simulation of project behaviour with the Abdel-Hamid software project model. These models give clues to the decision structures that are necessary for good agreement with reality. The final simplified model, with five states, is a good match for the prime states of the Abdel-Hamid model, the NASA data, and compares favourably to the Ruiz model. The linear control system model has a much simpler structure, with the same limitations. Both the simple SD and control models are more suited to preliminary estimates of project performance.

scholarly journals CONTROLLING NETWORK DYNAMICS

2019 ◽  
Vol 22 (07n08) ◽  
pp. 1950021 ◽  
Author(s):  
AMING LI ◽  
YANG-YU LIU
Keyword(s):  
Rapid Development ◽  
Network Dynamics ◽  
Great Depth ◽  
Network Control ◽  
Networked Systems ◽  
Practical Applications ◽  
Dynamics And Control ◽  
Engineered Systems ◽  
And Control ◽  
Near Future

Network science has experienced unprecedented rapid development in the past two decades. The network perspective has also been widely applied to explore various complex systems in great depth. In the first decade, fundamental characteristics of complex network structure, such as the small-worldness, scale-freeness, and modularity, of various complex networked systems were harvested from analyzing big empirical data. The associated dynamical processes on complex networks were also heavily studied. In the second decade, more attention was devoted to investigating the control of complex networked systems, ranging from fundamental theories to practical applications. Here we briefly review the recent progress regarding network dynamics and control, mainly concentrating on research questions proposed in the six papers we collected for this topical issue. This review closes with possible research directions along this line, and several important problems to be solved. We expect that, in the near future, network control will play an even bigger role in more fields, helping us understand and control many complex natural and engineered systems.

scholarly journals A unified approach to rigid body rotational dynamics and control

2011 ◽  
Vol 468 (2138) ◽  
pp. 395-414 ◽  
Author(s):  
Firdaus E. Udwadia ◽  
Aaron D. Schutte
Keyword(s):  
Rigid Body ◽  
Closed Form ◽  
Equations Of Motion ◽  
Fundamental Equation ◽  
Rotational Dynamics ◽  
Constrained Motion ◽  
Dynamics And Control ◽  
Common Framework ◽  
The Stability ◽  
And Control

This paper develops a unified methodology for obtaining both the general equations of motion describing the rotational dynamics of a rigid body using quaternions as well as its control. This is achieved in a simple systematic manner using the so-called fundamental equation of constrained motion that permits both the dynamics and the control to be placed within a common framework. It is shown that a first application of this equation yields, in closed form, the equations of rotational dynamics, whereas a second application of the self-same equation yields two new methods for explicitly determining, in closed form, the nonlinear control torque needed to change the orientation of a rigid body. The stability of the controllers developed is analysed, and numerical examples showing the ease and efficacy of the unified methodology are provided.

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