2,975 research outputs found
Distributed Robust Consensus Control of Multi-agent Systems with Heterogeneous Matching Uncertainties
This paper considers the distributed consensus problem of linear multi-agent
systems subject to different matching uncertainties for both the cases without
and with a leader of bounded unknown control input. Due to the existence of
nonidentical uncertainties, the multi-agent systems discussed in this paper are
essentially heterogeneous. For the case where the communication graph is
undirected and connected, a distributed continuous static consensus protocol
based on the relative state information is first designed, under which the
consensus error is uniformly ultimately bounded and exponentially converges to
a small adjustable residual set. A fully distributed adaptive consensus
protocol is then designed, which, contrary to the static protocol, relies on
neither the eigenvalues of the Laplacian matrix nor the upper bounds of the
uncertainties. For the case where there exists a leader whose control input is
unknown and bounded, distributed static and adaptive consensus protocols are
proposed to ensure the boundedness of the consensus error. It is also shown
that the proposed protocols can be redesigned so as to ensure the boundedness
of the consensus error in the presence of bounded external disturbances which
do not satisfy the matching condition. A sufficient condition for the existence
of the proposed protocols is that each agent is stabilizable.Comment: 16 page, 10 figures. This manuscript is an extended version of our
paper accepted for publication by Automatic
Macroscopic Noisy Bounded Confidence Models with Distributed Radical Opinions
In this article, we study the nonlinear Fokker-Planck (FP) equation that
arises as a mean-field (macroscopic) approximation of bounded confidence
opinion dynamics, where opinions are influenced by environmental noises and
opinions of radicals (stubborn individuals). The distribution of radical
opinions serves as an infinite-dimensional exogenous input to the FP equation,
visibly influencing the steady opinion profile. We establish mathematical
properties of the FP equation. In particular, we (i) show the well-posedness of
the dynamic equation, (ii) provide existence result accompanied by a
quantitative global estimate for the corresponding stationary solution, and
(iii) establish an explicit lower bound on the noise level that guarantees
exponential convergence of the dynamics to stationary state. Combining the
results in (ii) and (iii) readily yields the input-output stability of the
system for sufficiently large noises. Next, using Fourier analysis, the
structure of opinion clusters under the uniform initial distribution is
examined. Specifically, two numerical schemes for identification of
order-disorder transition and characterization of initial clustering behavior
are provided. The results of analysis are validated through several numerical
simulations of the continuum-agent model (partial differential equation) and
the corresponding discrete-agent model (interacting stochastic differential
equations) for a particular distribution of radicals
Distributed Bayesian Filtering using Logarithmic Opinion Pool for Dynamic Sensor Networks
The discrete-time Distributed Bayesian Filtering (DBF) algorithm is presented
for the problem of tracking a target dynamic model using a time-varying network
of heterogeneous sensing agents. In the DBF algorithm, the sensing agents
combine their normalized likelihood functions in a distributed manner using the
logarithmic opinion pool and the dynamic average consensus algorithm. We show
that each agent's estimated likelihood function globally exponentially
converges to an error ball centered on the joint likelihood function of the
centralized multi-sensor Bayesian filtering algorithm. We rigorously
characterize the convergence, stability, and robustness properties of the DBF
algorithm. Moreover, we provide an explicit bound on the time step size of the
DBF algorithm that depends on the time-scale of the target dynamics, the
desired convergence error bound, and the modeling and communication error
bounds. Furthermore, the DBF algorithm for linear-Gaussian models is cast into
a modified form of the Kalman information filter. The performance and robust
properties of the DBF algorithm are validated using numerical simulations
Neural Network Observer-Based Prescribed-Time Fault-Tolerant Tracking Control for Heterogeneous Multiagent Systems With a Leader of Unknown Disturbances
This study investigates the prescribed-time leader-follower formation strategy for heterogeneous multiagent sys-tems including unmanned aerial vehicles and unmanned ground vehicles under time-varying actuator faults and unknown dis-turbances based on adaptive neural network observers and backstepping method. Compared with the relevant works, the matching and mismatched disturbances of the leader agent are further taken into account in this study. A distributed fixed-time observer is developed for follower agents in order to timely obtain the position and velocity states of the leader, in which neural networks are employed to approximate the unknown disturbances. Furthermore, the actual sensor limitations make each follower only affected by local information and measurable local states. As a result, another fixed-time neural network observer is proposed to obtain the unknown states and the complex uncertainties. Then, a backstepping prescribed-time fault-tolerant formation controller is constructed by utilizing the estimations, which not only guarantees that the multiagent systems realize the desired formation configuration in a user-assignable finite time, but also ensures that the control action can be smooth everywhere. Finally, simulation examples are designed to testify the validity of the developed theoretical method
Decentralized Output Sliding-Mode Fault-Tolerant Control for Heterogeneous Multiagent Systems
This paper proposes a novel decentralized output sliding-mode fault-tolerant control (FTC) design for heterogeneous multiagent systems (MASs) with matched disturbances, unmatched nonlinear interactions, and actuator faults. The respective iteration and iteration-free algorithms in the sliding-mode FTC scheme are designed with adaptive upper bounding laws to automatically compensate the matched and unmatched components. Then, a continuous fault-tolerant protocol in the observer-based integral sliding-mode design is developed to guarantee the asymptotic stability of MASs and the ultimate boundedness of the estimation errors. Simulation results validate the efficiency of the proposed FTC algorithm
Distributed Adaptive Control for a Class of Heterogeneous Nonlinear Multi-Agent Systems with Nonidentical Dimensions
A novel feedback distributed adaptive control strategy based on radial basis neural network (RBFNN) is proposed for the consensus control of a class of leaderless heterogeneous nonlinear multi-agent systems with the same and different dimensions. The distributed control, which consists of a sequence of comparable matrices or vectors, can make that all the states of each agent to attain consensus dynamic behaviors are defined with similar parameters of each agent with nonidentical dimensions. The coupling weight adaptation laws and the feedback management of neural network weights ensure that all signals in the closed-loop system are uniformly ultimately bounded. Finally, two simulation examples are carried out to validate the effectiveness of the suggested control design strategy
Fully distributed consensus for high-order strict-feedback nonlinear multiagent systems with switched topologies
summary:This paper studies the distributed consensus problem of high-order strict-feedback nonlinear multiagent systems. By employing the adaptive backstepping technique and switched system theory, a novel protocol is proposed for MASs with switched topologies. Global information such as the number of agents and communication topology is not used. In addition, the communication topology between agents can be switched between possible topologies at any time. Based on the Lyapunov function method, the proposed adaptive protocol guarantees the complete consensus of multiagent systems without restricting the dwell time of the switched signal. Finally, two numerical examples are provided to illustrate the effectiveness and advantages of the given protocol
Consensus Convergence with Stochastic Effects
We consider a stochastic, continuous state and time opinion model where each
agent's opinion locally interacts with other agents' opinions in the system,
and there is also exogenous randomness. The interaction tends to create
clusters of common opinion. By using linear stability analysis of the
associated nonlinear Fokker-Planck equation that governs the empirical density
of opinions in the limit of infinitely many agents, we can estimate the number
of clusters, the time to cluster formation and the critical strength of
randomness so as to have cluster formation. We also discuss the cluster
dynamics after their formation, the width and the effective diffusivity of the
clusters. Finally, the long term behavior of clusters is explored numerically.
Extensive numerical simulations confirm our analytical findings.Comment: Dedication to Willi J\"{a}ger's 75th Birthda
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