333 research outputs found
Bipartite Consensus for a Class of Nonlinear Multi-agent Systems Under Switching Topologies:A Disturbance Observer-Based Approach
This paper considers the leader-following bipartite consensus for a class of nonlinear multi-agent systems (MASs) subject to exogenous disturbances under directed fixed and switching topologies, respectively. Firstly, two new output feedback control protocols involving signs of link weights are introduced based on relative output measurements of neighboring agents. In order to estimate the disturbances produced by an exogenous system, a disturbance observer-based approach is developed. Then, sufficient conditions for leader-following bipartite consensus with directed fixed topologies are derived. Furthermore, by assuming that each switching topology contains a directed spanning tree, it is proved that the leader-following bipartite consensus can be realized with the designed output feedback control protocol if the dwell time is larger than a non-negative threshold. Finally, numerical simulations inspired by a real-world DC motors are provided to illustrate the effectiveness of the proposed controllers
Bipartite containment of heterogeneous multi-agent systems under denial-of-service attacks: a historical information-based control scheme
A distributed control scheme based on historical information is designed to solve the problem of stable control of multi-agent systems under denial of service (DoS) attacks in this article. It achieves the control objective of bipartite output containment control, that is, the output states of the followers smoothly enter the target area. The control scheme updates the states of followers through historical information in the control protocol when agents are subjected to DoS attacks. A distributed state observer with a storage module is designed to efficiently estimate the state of followers and store the observed information as history information. The historical information of control protocol calls is not necessarily the real state information in the existence of DoS attacks. Consequently, a closed-loop feedback state compensator is designed. Then, the state compensator is converted from the time domain to the frequency domain for stability analysis using the Nyquist criterion. It is obtained that an upper bound on the amount of historical information can achieve the bipartite output trajectories containment of the controlled system. The output trajectories of the followers converge into two dynamic convex hulls, one of which is surrounded by multiple leaders, and the other is a convex hull with opposite signs of the leaders. Finally, a numerical simulation is used to verify the proposed control scheme, and the operability of the scheme is further demonstrated in a physical experiment
High-Order Leader-Follower Tracking Control under Limited Information Availability
Limited information availability represents a fundamental challenge for
control of multi-agent systems, since an agent often lacks sensing capabilities
to measure certain states of its own and can exchange data only with its
neighbors. The challenge becomes even greater when agents are governed by
high-order dynamics. The present work is motivated to conduct control design
for linear and nonlinear high-order leader-follower multi-agent systems in a
context where only the first state of an agent is measured. To address this
open challenge, we develop novel distributed observers to enable followers to
reconstruct unmeasured or unknown quantities about themselves and the leader
and on such a basis, build observer-based tracking control approaches. We
analyze the convergence properties of the proposed approaches and validate
their performance through simulation
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
Prescribed Time Time-varying Output Formation Tracking for Uncertain Heterogeneous Multi-agent Systems
The time-varying output formation tracking for the heterogeneous multi-agent
systems (MAS) is investigated in this paper. First, a distributed observer is
constructed for followers to estimate the states of the leader, which can
ensure that the estimation error converges to the origin in the prescribed
time. Then, the local formation controller is designed for each follower based
on the estimation of the observer, under which, the formation errors converge
to the origin in the prescribed time as well. That is, the settling time of the
whole system can be predefined in advance. It's noted that not only the
uncertainties in the state matrix but also the uncertainties in the input
matrix are considered, which makes the problem more practical. Last, a
simulation is performed to show the effectiveness of the proposed approach
Learning-based Robust Bipartite Consensus Control for a Class of Multiagent Systems
This paper studies the robust bipartite consensus problems for heterogeneous nonlinear nonaffine discrete-time multi-agent systems (MASs) with fixed and switching topologies against data dropout and unknown disturbances. At first, the controlled system's virtual linear data model is developed by employing the pseudo partial derivative technique, and a distributed combined measurement error function is established utilizing a signed graph theory. Then, an input gain compensation scheme is formulated to mitigate the effects of data dropout in both feedback and forward channels. Moreover, a data-driven learning-based robust bipartite consensus control (LRBCC) scheme based on a radial basis function neural network observer is proposed to estimate the unknown disturbance, using the online input/output data without requiring any information on the mathematical dynamics. The stability analysis of the proposed LRBCC approach is given. Simulation and hardware testing also illustrate the correctness and effectiveness of the designed method
Synchronization of heterogeneous harmonic oscillators for generalized uniformly jointly connected networks
The synchronization problem for heterogeneous harmonic oscillators is investigated. In practice, the communication network among oscillators might suffer from equipment failures or malicious attacks. The connection may switch extremely frequently without dwell time, and can thus be described by generalized uniformly jointly connected networks. We show that the presented typical control law is strongly robust against various unreliable communications. Combined with the virtual output approach and generalized Krasovskii-LaSalle theorem, the stability is proved with the help of its cascaded structure. Numerical examples are presented to show the correctness of the control law
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