144 research outputs found
Synchronization Problems in Networks of Nonlinear Agents
Over the last years, consensus and synchronization problems have been a popular
topic in the systems and control community. This interest is motivated by the fact that, in several fields of application, a certain number of agents is interacting or has to cooperate to achieve a certain task. Robotic swarms, sensor networks, power networks, biological networks are only few outstanding examples where networks of agents displays behaviors which can be modeled and studied by means of consensus and synchronisation techniques.
In this thesis we consider a general class of networked nonlinear systems in different operating frameworks and design control architecture to force the systems to reach synchronization and consensus on a target behavior. In particular, we consider the case of homogeneous and heterogeneous nonlinear agents with a static communication topology and design a static high-gain-based diffusive coupling and an internal model-based regulator respectively, to solve the problem of consensus. Then, we analyze the case of dynamical links and show under which conditions, synchronization for homogeneous nonlinear systems can be achieved. Depending on the structure of the dynamic links at hand, static and dynamic regulators (based on the concept extended state observers) are proposed.
Furthermore, we address the problem of disconnected topology and switching topology and derive under which conditions agents reach cluster synchronization and synchronization respectively. Last, we consider the problem of a sampled exchange of information between the agents and design a triggering rule locally at each agent such that the overall network reaches synchronization
A Review of Consensus-based Multi-agent UAV Applications
In this paper, a review of distributed control for multi-agent systems is proposed, focusing on consensus-based applications. Both rotary-wing and fixed-wing Unmanned Aerial Vehicles (UAVs) are considered. On one side, methodologies and implementations based on collision and obstacle avoidance through consensus are analyzed for multirotor UAVs. On the other hand, a target tracking through consensus is considered for fixed-wing UAVs. This novel approach to classify the literature could help researchers to assess the outcomes achieved in these two directions in view of potential practical implementations of consensus-based methodologies
Adaptive consensus based formation control of unmanned vehicles
Over the past decade, the control research community has given significant attention to formation control of multiple unmanned vehicles due to a variety of commercial and defense applications. Consensus-based formation control is considered to be more robust and reliable when compared to other formation control methods due to scalability and inherent properties that enable the formation to continue even if one of the vehicles experiences a failure. In contrast to existing methods on formation control where the dynamics of the vehicles are neglected, this dissertation in the form of four papers presents consensus-based formation control of unmanned vehicles-both ground and aerial, by incorporating the vehicle dynamics.
First, neural networks (NN)-based optimal adaptive consensus-based formation control over finite horizon is presented for networked mobile robots or agents in the presence of uncertain robot/agent dynamics and communication. In the second paper, a hybrid automaton is proposed to control the nonholonomic mobile robots in two discrete modes: a regulation mode and a formation keeping mode in order to overcome well-known stabilization problem. The third paper presents the design of a distributed consensus-based event-triggered formation control of networked mobile robots using NN in the presence of uncertain robot dynamics to minimize communication. All these papers assume state availability.
Finally, the fourth paper extends the consensus effort by introducing the development of a novel nonlinear output feedback NN-based controller for a group of quadrotor UAVs --Abstract, page iv
Adaptive leaderless consensus of agents in jointly connected networks
In this paper, the leaderless consensus problem of multi-agent systems with
jointly connected topologies and nonlinear dynamics is considered, in which
the nonlinear dynamics are assumed to be non-identical and unknown. The
unknown nonlinear dynamics existing in the systems are assumed to be linearly
parameterized, and an adaptive design method for leaderless multiagent
systems is presented. By just using the relative position information
between each agent and its neighbours, a distributed adaptive consensus control
algorithm for the considered systems is proposed, in which the network
graphs are jointly connected. Both the global uniform asymptotical stability
and the global uniform asymptotical parameter convergence analysis of the
adaptive control algorithm are carried out by using adaptive control theory,
Lyapunov theory and algebraic graph theory. Finally, an example is given to
illustrate the validity of our theoretical results.The National Natural Science Foundation
(NNSF) of China (61273183, 61374028 and 61304162).http://www.elsevier.com/locate/neucom2018-06-30hb2017Electrical, Electronic and Computer Engineerin
COOPERATIVE AND CONSENSUS-BASED CONTROL FOR A TEAM OF MULTI-AGENT SYSTEMS
Cooperative control has attracted a noticeable interest in control systems
community due to its numerous applications in areas such as formation flying
of unmanned aerial vehicles, cooperative attitude control of spacecraft, rendezvous
of mobile robots, unmanned underwater vehicles, traffic control, data
network congestion control and routing. Generally, in any cooperative control
of multi-agent systems one can find a set of locally sensed information, a
communication network with limited bandwidth, a decision making algorithm,
and a distributed computational capability. The ultimate goal of cooperative
systems is to achieve consensus or synchronization throughout the team members
while meeting all communication and computational constraints. The
consensus problem involves convergence of outputs or states of all agents to
a common value and it is more challenging when the agents are subjected to
disturbances, measurement noise, model uncertainties or they are faulty.
This dissertation deals with the above mentioned challenges and has developed
methods to design distributed cooperative control and fault recovery
strategies in multi-agent systems. Towards this end, we first proposed a
transformation for Linear Time Invariant (LTI) multi-agent systems that facilitates
a systematic control design procedure and make it possible to use
powerful Lyapunov stability analysis tool to guarantee its consensus achievement.
Moreover, Lyapunov stability analysis techniques for switched systems
are investigated and a novel method is introduced which is well suited for designing
consensus algorithms for switching topology multi-agent systems. This
method also makes it possible to deal with disturbances with limited root mean
square (RMS) intensities. In order to decrease controller design complexity, a
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method is presented which uses algebraic connectivity of the communication
network to decouple augmented dynamics of the team into lower dimensional
parts, which allows one to design the consensus algorithm based on the solution
to an algebraic Riccati equation with the same order as that of agent.
Although our proposed decoupling method is a powerful approach to reduce
the complexity of the controller design, it is possible to apply classical pole
placement methods to the transformed dynamics of the team to develop and
obtain controller gains.
The effects of actuator faults in consensus achievement of multi-agent systems
is investigated. We proposed a framework to quantitatively study actuator
loss-of-effectiveness effects in multi-agent systems. A fault index is defined
based on information on fault severities of agents and communication network
topology, and sufficient conditions for consensus achievement of the team are
derived. It is shown that the stability of the cooperative controller is linked to
the fault index. An optimization problem is formulated to minimize the team
fault index that leads to improvements in the performance of the team. A numerical
optimization algorithm is used to obtain the solutions to the optimal
problem and based on the solutions a fault recovery strategy is proposed for
both actuator saturation and loss-of-effectiveness fault types.
Finally, to make our proposed methodology more suitable for real life scenarios,
the consensus achievement of a multi-agent team in presence of measurement
noise and model uncertainties is investigated. Towards this end, first
a team of LTI agents with measurement noise is considered and an observer
based consensus algorithm is proposed and shown that the team can achieve
H∞ output consensus in presence of both bounded RMS disturbance input and
measurement noise. In the next step a multi-agent team with both linear and
Lipschitz nonlinearity uncertainties is studied and a cooperative control algorithm
is developed. An observer based approach is also developed to tackle
consensus achievement problem in presence of both measurement noise and
model uncertainties
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Synchronization Control for Discrete-Time-Delayed Dynamical Networks with Switching Topology under Actuator Saturations
10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61773156, 61873148, 61673141 and 61933007); 10.13039/501100018551-Program for Science and Technology Innovation Talents in the Universities of Henan Province of China (Grant Number: 19HASTIT028); 10.13039/501100010029-Research Fund for the Taishan Scholar Project of Shandong Province of China; 10.13039/501100000288-Royal Society of the U.K.; 10.13039/100005156-Alexander von Humboldt Foundation of Germany
Coordination of multi-agent systems: stability via nonlinear Perron-Frobenius theory and consensus for desynchronization and dynamic estimation.
This thesis addresses a variety of problems that arise in the study of complex networks composed by multiple interacting agents, usually called multi-agent systems (MASs). Each agent is modeled as a dynamical system whose dynamics is fully described by a state-space representation.
In the first part the focus is on the application to MASs of recent results that deal with the extensions of Perron-Frobenius theory to nonlinear maps. In the shift from the linear to the nonlinear framework, Perron-Frobenius theory considers maps being order-preserving instead of matrices being nonnegative. The main contribution is threefold. First of all, a convergence analysis of the iterative behavior of two novel classes of order-preserving nonlinear maps is carried out, thus establishing sufficient conditions which guarantee convergence toward a fixed point of the map: nonnegative row-stochastic matrices turns out to be a special case. Secondly, these results are applied to MASs, both in discrete and continuous-time: local properties of the agents' dynamics have been identified so that the global interconnected system falls into one of the above mentioned classes, thus guaranteeing its global stability. Lastly, a sufficient condition on the connectivity of the communication network is provided to restrict the set of equilibrium points of the system to the consensus points, thus ensuring the agents to achieve consensus. These results do not rely on standard tools (e.g., Lyapunov theory) and thus they constitute a novel approach to the analysis and control of multi-agent dynamical systems.
In the second part the focus is on the design of dynamic estimation algorithms in large networks which enable to solve specific problems. The first problem consists in breaking synchronization in networks of diffusively coupled harmonic oscillators. The design of a local state feedback that achieves desynchronization in connected networks with arbitrary undirected interactions is provided. The proposed control law is obtained via a novel protocol for the distributed estimation of the Fiedler vector of the Laplacian matrix. The second problem consists in the estimation of the number of active agents in networks wherein agents are allowed to join or leave. The adopted strategy consists in the distributed and dynamic estimation of the maximum among numbers locally generated by the active agents and the subsequent inference of the number of the agents that took part in the experiment. Two protocols are proposed and characterized to solve the consensus problem on the time-varying max value. The third problem consists in the average state estimation of a large network of agents where only a few agents' states are accessible to a centralized observer. The proposed strategy projects the dynamics of the original system into a lower dimensional state space, which is useful when dealing with large-scale systems. Necessary and sufficient conditions for the existence of a linear and a sliding mode observers are derived, along with a characterization of their design and convergence properties
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Robust Hybrid Systems for Control, Learning, and Optimization in Networked Dynamical Systems
The deployment of advanced real-time control and optimization strategies in socially-integratedengineering systems could significantly improve our quality of life whilecreating jobs and economic opportunity. However, in cyber-physical systems such assmart grids, transportation networks, healthcare, and robotic systems, there still existseveral challenges that prevent the implementation of intelligent control strategies.These challenges include the existence of limited communication networks, dynamicand stochastic environments, multiple decision makers interacting with the system,and complex hybrid dynamics emerging from the feedback interconnection of physicalprocesses and computational devices.In this dissertation, we study the problem of designing robust control and optimizationalgorithms for cyber-physical systems using the framework of hybrid dynamicalsystems. We propose different theoretical frameworks for the design and analysis offeedback mechanisms that optimize the performance of dynamical systems without requiringan explicit characterization of their mathematical model, i.e., in a model-freeway. The closed-loop system that emerges of the interconnection of the plant with thefeedback mechanism describes, in general, a set-valued hybrid dynamical system. Thesetypes of systems combine continuous-time and discrete-time dynamics, and they usuallylack the uniqueness of solutions property. The framework of set-valued hybriddynamical systems allows us to study many complex dynamical systems that emerge indifferent engineering applications, such as networked multi-agent systems with switching graphs, non-smooth mechanical systems, dynamic pricing mechanisms in transportationsystems, autonomous robots with logic-based controllers, etc. We proposea step-by-step approach to the design of different types of discrete-time, continuous-time,hybrid, and stochastic controllers for different types of applications, extendingand generalizing different results in the literature in the area of extremum seeking control,sampled-data extremization, robust synchronization, and stochastic learning innetworked systems. Our theoretical results are illustrated via different simulations andnumerical examples
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