Related Experiment Video
Updated: May 17, 2025
08:08
Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
Published on: June 24, 2015
11.4K
Linear programming-based stabilization and synchronization of positive complex networks with dynamic link subsystems.
Shouting Hong1, Junfeng Zhang1, Gang Zheng2
1School of Information and Communication Engineering, Hainan University, Haikou, China.
Plos One
|May 15, 2025
Summary
This study stabilizes and synchronizes positive complex networks with dynamic links using novel controllers and coupling terms. The methods ensure network stability and achieve synchronization, verified by simulations.
Area of Science:
- Complex networks
- Control theory
- Systems engineering
Background:
- Complex networks are fundamental to many systems.
- Dynamic links introduce challenges in network stability and synchronization.
- Existing methods may not adequately address positive complex networks with dynamic links.
Purpose of the Study:
- To investigate the stabilization and synchronization of positive complex networks with dynamic links.
- To design controllers and coupling terms for network stability and synchronization.
- To develop a tractable framework for analysis and computation.
Main Methods:
- Construction of positive complex networks with dynamic links.
- Design of controllers and coupling terms for stability and synchronization.
- Application of linear programming and copositive Lyapunov functions for analysis.
Main Results:
- A novel coupling term effectively achieves stability and synchronization.
- A comprehensive framework for stabilization and synchronization is established.
- A computationally tractable method for design and analysis is introduced.
Conclusions:
- The proposed approaches effectively stabilize and synchronize positive complex networks with dynamic links.
- The developed framework offers a robust solution for network control.
- The simulation results validate the practical feasibility of the presented methods.
Related Concept Videos
01:25Multimachine Stability
115
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
115
01:24Pole and System Stability
224
The transfer function is a fundamental concept representing the ratio of two polynomials. The numerator and denominator encapsulate the system's dynamics. The zeros and poles of this transfer function are critical in determining the system's behavior and stability.
Simple poles are unique roots of the denominator polynomial. Each simple pole corresponds to a distinct solution to the system's characteristic equation, typically resulting in exponential decay terms in the system's...
Simple poles are unique roots of the denominator polynomial. Each simple pole corresponds to a distinct solution to the system's characteristic equation, typically resulting in exponential decay terms in the system's...
224
01:24BIBO stability of continuous and discrete -time systems
309
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
309
01:23Linear time-invariant Systems
190
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
190
01:23Root Loci for Positive-Feedback Systems
80
The Hartley oscillator is a positive feedback system that sustains oscillations by feeding the output back to the input in phase, thereby reinforcing the signal. Positive feedback systems can be viewed as negative feedback systems with inverted feedback signals. In these systems, the root locus encompasses all points on the s-plane where the angle of the system transfer function equals 360 degrees.
The construction rules for the root locus in positive feedback systems are similar to those in...
The construction rules for the root locus in positive feedback systems are similar to those in...
80
01:14Stability of structures
148
In mechanical engineering, the stability of systems under various forces is critical for designing durable and efficient structures. One fundamental way to explore these concepts is by analyzing systems like two rods connected at a pivot point, O, with a torsional spring of spring constant k at the pivot point. This system is similar in appearance to a scissor jack used to change tires on a car. In this case, the arms of the linkage (equivalent to the rods in this system) are entirely vertical,...
148