scholarly journals Synchronization of Complex Dynamical Networks with Nonidentical Nodes and Derivative Coupling via Distributed Adaptive Control

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Miao Shi ◽  
Junmin Li ◽  
Chao He
Keyword(s):  
Adaptive Control ◽  
Adaptive Learning ◽  
Closed Loop ◽  
Adaptive Synchronization ◽  
Synchronization Control ◽  
Complex Dynamical Network ◽  
Error Norm ◽  
Derivative Coupling ◽  
Distributed Adaptive Control ◽  
Nonidentical Nodes

Adaptive synchronization control is proposed for a new complex dynamical network model with nonidentical nodes and nonderivative and derivative couplings. The distributed adaptive learning laws of periodically time-varying and constant parameters and distributed adaptive control are designed. The new method which can obtain the synchronization error of closed-loop complex network system is asymptotic convergence in the sense of square error norm. What is more, the coupling matrix is not assumed to be symmetric or irreducible. Finally, a simulation example shows the feasibility and effectiveness of the approach.

scholarly journals Distributed Adaptive Synchronization for Complex Dynamical Networks with Uncertain Nonlinear Neutral-Type Coupling

2013 ◽  
Vol 2013 ◽  
pp. 1-12
Author(s):  
Shi Miao ◽  
Li Junmin
Keyword(s):  
Neutral Type ◽  
Complex Dynamical Networks ◽  
Adaptive Synchronization ◽  
Synchronization Control ◽  
Nonlinear Functions ◽  
Error Norm ◽  
Dynamical Networks ◽  
Derivative Coupling ◽  
Time Varying Parameters ◽  
Type Coupling

Distributed adaptive synchronization control for complex dynamical networks with nonlinear derivative coupling is proposed. The distributed adaptive strategies are constituted by directed connections among nodes. By means of the parameters separation, the nonlinear functions can be transformed into the linearly form. Then effective distributed adaptive techniques are designed to eliminate the effect of time-varying parameters and made the considered network synchronize a given trajectory in the sense of square error norm. Furthermore, the coupling matrix is not assumed to be symmetric or irreducible. An example shows the applicability and feasibility of the approach.

scholarly journals Adaptive Synchronization of Nonlinearly Parameterized Complex Dynamical Networks with Unknown Time-Varying Parameters

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Tengfei Wang ◽  
Junmin Li ◽  
Shu Tang
Keyword(s):  
Adaptive Learning ◽  
Complex Dynamical Networks ◽  
Adaptive Synchronization ◽  
Time Varying ◽  
Error Norm ◽  
Dynamical Networks ◽  
Varying Parameters ◽  
Time Varying Parameters ◽  
Nonlinearly Parameterized ◽  
Composite Energy

A new adaptive learning control approach is proposed for a class of nonlinearly parameterized complex dynamical networks with unknown time-varying parameters. By using the parameter separation and reparameterization technique, the adaptive learning laws of periodically time-varying and constant parameters and an adaptive control strategy are designed to ensure the asymptotic convergence of the synchronization error in the sense of square error norm. Then, a sufficient condition of the synchronization is given by constructing a composite energy function. Finally, an example of the complex network is used to verify the effectiveness of proposed approach.

Mitigating the Effects of Sensor Uncertainties in Networked Multi-Agent Systems

2017 ◽  
Vol 139 (4) ◽  
Author(s):  
Ehsan Arabi ◽  
Tansel Yucelen ◽  
Wassim M. Haddad
Keyword(s):  
Adaptive Control ◽  
System Performance ◽  
Closed Loop ◽  
Control Architecture ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Distributed Adaptive Control ◽  
Exogenous Disturbances ◽  
Multi Agent ◽  
System Uncertainties

Networked multi-agent systems consist of interacting agents that locally exchange information, energy, or matter. Since these systems do not in general have a centralized architecture to monitor the activity of each agent, resilient distributed control system design for networked multi-agent systems is essential in providing high system performance, reliability, and operation in the presence of system uncertainties. An important class of such system uncertainties that can significantly deteriorate the achievable closed-loop system performance is sensor uncertainties, which can arise due to low sensor quality, sensor failure, sensor bias, or detrimental environmental conditions. This paper presents a novel distributed adaptive control architecture for networked multi-agent systems with undirected communication graph topologies to mitigate the effect of sensor uncertainties. Specifically, we consider agents having identical high-order, linear dynamics with agent interactions corrupted by unknown exogenous disturbances. We show that the proposed adaptive control architecture guarantees asymptotic stability of the closed-loop dynamical system when the exogenous disturbances are time-invariant and uniform ultimate boundedness when the exogenous disturbances are time-varying. Two numerical examples are provided to illustrate the efficacy of the proposed distributed adaptive control architecture.

A chronology of Distributed Adaptive Control

2018 ◽  
Author(s):  
Paul F. M. J. Verschure
Keyword(s):  
Adaptive Control ◽  
Human Robot Interaction ◽  
Integration Framework ◽  
The Social ◽  
Distributed Adaptive Control ◽  
Core Components ◽  
Mind And Brain ◽  
Convergent Validation ◽  
The Mind ◽  
And Behavior

This chapter presents the Distributed Adaptive Control (DAC) theory of the mind and brain of living machines. DAC provides an explanatory framework for biological brains and an integration framework for synthetic ones. DAC builds on several themes presented in the handbook: it integrates different perspectives on mind and brain, exemplifies the synthetic method in understanding living machines, answers well-defined constraints faced by living machines, and provides a route for the convergent validation of anatomy, physiology, and behavior in our explanation of biological living machines. DAC addresses the fundamental question of how a living machine can obtain, retain, and express valid knowledge of its world. We look at the core components of DAC, specific benchmarks derived from the engagement with the physical and the social world (the H4W and the H5W problems) in foraging and human–robot interaction tasks. Lastly we address how DAC targets the UTEM benchmark and the relation with contemporary developments in AI.

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