Class Of Uncertain Multi-agent Systems Research Articles

Output-Feedback Consensus Maneuvering of Uncertain MIMO Strict-Feedback Multiagent Systems Based on a High-Order Neural Observer.

In this article, a distributed output-feedback consensus maneuvering problem is investigated for a class of uncertain multiagent systems with multi-input and multi-output (MIMO) strict-feedback dynamics. The followers are subject to immeasurable states and external disturbances. A distributed neural observer-based adaptive control method is designed for consensus maneuvering of uncertain MIMO multiagent systems. The method is based on a modular structure, resulting in the separation of three modules: 1) a variable update law for the parameterized path; 2) a high-order neural observer; and 3) an output-feedback consensus maneuvering control law. The proposed distributed neural observer-based adaptive control method ensures that all followers agree on a common motion guided by a desired parameterized path, and the proposed method evades adopting the adaptive backstepping or dynamic surface control design by reformulating the dynamics of agents, thereby reducing the complexity of the control structure. Combined with the cascade system analysis and interconnection system analysis, the input-to-state stability of the consensus maneuvering closed loop is established in the Lyapunov sense. A simulation example is presented to demonstrate the performance of the proposed distributed neural observer-based adaptive control method for output-feedback consensus maneuvering.

Formation Tracking for a Class of Uncertain Multi-Agent Systems: A Distributed Kalman Filtering Approach

Formation Tracking for a Class of Uncertain Multi-Agent Systems: A Distributed Kalman Filtering Approach

Adaptive Containment Control for a Class of Uncertain Multi-agent Systems With Unknown Virtual Control Gain Functions

Adaptive Containment Control for a Class of Uncertain Multi-agent Systems With Unknown Virtual Control Gain Functions

Fully Distributed Control for a Class of Uncertain Multi-Agent Systems with a Directed Topology and Unknown State-Dependent Control Coefficients

To address the control of uncertain multi-agent systems (MAS) with completely unknown system nonlinearities and unknown control coefficients, a global consensus method is proposed by constructing novel filters and barrier function-based distributed controllers. The main contributions are as follows. Firstly, a novel two-order filter is designed for each agent to produce informational estimates from the leader, such that a connectivity matrix is not used in the controller’s design, solving the difficultly caused by the time-varying control coefficients in a MAS with a directed graph. Secondly, combined with the novel filters, barrier functions are used to construct the distributed controller to deal with the completely unknown system nonlinearities, resulting in the global consensus of the MAS. Finally, it is rigorously proved that the consensus of the MAS is achieved while guaranteeing the prescribed tracking-error performance. Two examples are given to verify the effectiveness of the proposed method, in which the simulation results demonstrate the claims.

Adaptive Neural Control for a Class of Nonlinear Multiagent Systems.

This article studies the adaptive neural controller design for a class of uncertain multiagent systems described by ordinary differential equations (ODEs) and beams. Three kinds of agent models are considered in this study, i.e., beams, nonlinear ODEs, and coupled ODE and beams. Both beams and ODEs contain completely unknown nonlinearities. Moreover, the control signals are assumed to suffer from a class of generalized backlash nonlinearities. First, neural networks (NNs) are adopted to approximate the completely unknown nonlinearities. New barrier Lyapunov functions are constructed to guarantee the compact set conditions of the NNs. Second, new adaptive neural proportional integral (PI)-type controllers are proposed for the networked ODEs and beams. The parameters of the PI controllers are adaptively tuned by NNs, which can make the system output remain in a prescribed time-varying constraint. Two illustrative examples are presented to demonstrate the advantages of the obtained results.

Neural-Network-Based Fully Distributed Adaptive Consensus for a Class of Uncertain Multiagent Systems

In this article, we revisit the problem of distributed neuroadaptive consensus for uncertain multiagent systems (MASs) in the presence of unmodeled nonlinearities as well as unknown disturbances. Robust consensus controllers comprising a linear feedback term, a discontinuous feedback term, and a neural network approximation term are constructed, where in each term, the weight part is endowed with some dynamical changing law. The asymptotic convergence of the consensus errors is theoretically proved based on the graph theory, nonsmooth analysis, and Barbalat's lemma. Both leaderless consensus and leader-follower tracking problems are considered before the results are further extended to containment problem in the presence of multileaders. A dramatic feature of the proposed method, in comparison with related works, is the fully distributed fashion of the information, requiring neither the underlying Laplacian eigenvalues nor the input upper bounds of the leaders (if exist). Several numerical examples are presented to testify the theoretical results.

Data-Based Fault-Tolerant Consensus Control for Uncertain Multiagent Systems Via Weighted Edge Dynamics

This paper is concerned with the fault-tolerant consensus control for a class of uncertain multiagent systems (MASs) via weighted edge dynamics. Different from the existing consensus results, the considered communication graph is weighted and the system model is assumed to be completely unknown. First, based on an important factorization of Laplacian matrix, a novel system named as weighted edge dynamics is introduced, and it is then proved that the stabilizing controller design for the transformed weighted edge dynamics is equivalent to the consensus controller design for the initial MAS. Within this framework, an efficient data-based algorithm is given to derive the fault-tolerant stabilizing controller, and the consensus of the initial MAS can be further ensured under the designed control protocol. Finally, some simulation examples on the LC oscillator networks are given to demonstrate the validity of theoretical results.

Finite-time stability for uncertain differential equations: a first investigation on a new class of multi-agent systems

In this paper, we discuss a new kind of stability, that is, finite-time stability, for uncertain differential equations, by formalizing some properties. As a possible application, we define a new class of uncertain multi-agent systems, according to the Liu’s uncertainty theory, as a counterpart of stochastic multi-agent systems. We formalize the governing equations, driven by canonical process, which is a type of uncertain process with stationary and independent increments. The concept of finite-time consensus in the context of uncertainty theory is consequently derived. A numerical procedure to estimate the settling time is proposed. The case with proportional delay was also considered.

Neuro‐adaptive consensus strategy for a class of nonlinear time‐delay multi‐agent systems with an unmeasurable high‐dimensional leader

Distributed cooperative consensus tracking problem for a class of uncertain multi-agent systems with a high-dimensional leader under a directed communication topology is concerned. Compared with related works, the dynamics of the leader agent is allowed to be different from those of the followers and may not be measured. Meanwhile, the dynamics of each follower is subject to un-modelled dynamics, unknown time-varying delays, as well as external disturbances, which makes the model more suitable in various practical applications. Based on the M-matrix and Lyapunov-Krasovskii functional method, a distributed robust radial basis function neural network controller as well as a local observer is designed for each follower so as to guarantee the ultimate boundness of the tracking errors to the leader's output signals. By appropriately cutting down the neural network parameters to be updated online, the computational burden can be greatly reduced. The effectiveness of the proposed consensus approach is testified via a numerical example.

Insensitive leader-following consensus for a class of uncertain multi-agent systems against actuator faults

This paper is concerned with the leader-following consensus control problem for a class of multi-agent systems against actuator faults and some perturbed factors as state-dependent uncertainties and multiplicative controller coefficient variations. The follower agents are supported to be connected by a multi-layer network structure, which can be described by a directed graph among different layers and an undirected graph in the same layer. Without requirement of knowledge of eventual faulty factors of actuators and perturbed factors of agents, a class of distributed controllers is constructed to ensure that follower agents can track the leader agent based on adaptive updates. Lyapunov functions are addressed to prove that asymptotic consensus of multi-agent systems can be achieved in the presence of partial loss of effectiveness on bias-actuators and uncertainties in agents and controllers. Finally, multiple coupled aircrafts are used to verify the efficiency of the proposed method.

Distributed Coordinated Tracking Control for a Class of Uncertain Multiagent Systems

This technical note studies the distributed coordinated tracking control for multiagent systems with model uncertainties. Both unknown model parameters and unknown system dynamics are considered. It is assumed that there exist parametric uncertainties and unknown dynamics with the informed agent as well, and only the state value of the informed agent can be accessed by a limited number of agents. With the utilization of neural network approximation and adaptive estimation, a new distributed adaptive tracking control is proposed to make all agents cooperatively follow the desired trajectory specified by the informed agent. The control design is first presented for the first-order multiagent systems, and then extension is made to the second-order multiagent systems using backstepping. A unique feature of the proposed control is that the unknown bounds of neural network approximation errors are also estimated online. Using Lyapunov stability theorem, it is rigorously proved that asymptotically cooperative tracking can be achieved under the assumption that the sensing/communication topology among agents is connected. Simulation results are included to illustrate the proposed control.

A continuous leader-following consensus control strategy for a class of uncertain multi-agent systems

In this paper, we study the robust leader-following consensus problem for a class of multi-agent systems with unknown nonlinear dynamics and unknown but bounded disturbances. The control input of the leader agent is nonzero and not available to any follower agent. We first consider a class of high order chain integrator-type multi-agent systems. By employing the robust integral of the sign of the error technique, a continuous distributed control law is constructed using local information obtained from neighboring agents. Using Lyapunov analysis theory, we show that under a connected undirected information communication topology, the proposed protocol achieves semiglobal leader-following consensus. We then extend the approach to a class of more general uncertain multiagent systems. A numerical example is given to verify our proposed protocol.

Cooperative robust output regulation of a class of heterogeneous linear uncertain multi-agent systems

SUMMARY The cooperative output regulation of a linear multi-agent system can be viewed as a generalization of the leader-following consensus problem and was studied recently for the case where the system uncertain parameters vary in a sufficiently small neighborhood of their nominal value. This case was handled by the internal model design which converts the problem into a simultaneous eigenvalue placement problem of an augmented multi-agent system. In this paper, we further consider the cooperative robust output regulation problem for a class of minimum phase linear multi-agent systems in the sense that the controller allows the system uncertain parameters to vary in an arbitrarily prescribed compact subset. For this purpose, we introduce a new type of internal model that allows the cooperative robust output regulation problem of the given plant to be converted into a robust stabilization problem of an augmented multi-agent system. We then solve our problem by combining a simultaneous high-gain state feedback control technique and a distributed high-gain observer technique. A special case of our result leads to the solution of the leader-following robust consensus problem for a class of uncertain multi-agent systems. Copyright © 2013 John Wiley & Sons, Ltd.

Controlled Consensus Criteria for a Class of Uncertain Multi-Agent Systems

AbstractIn this paper, the consensus problem for a class of uncertain Multi-Agent Systems will be discussed. Based on the network connection topology and Lyapunov stability theory, the local and global decentralized controlled consensus problems for such systems will be investigated. Under the different assumptions, several state feedback and output feedback consensus criteria are deduced. Moreover, the assumptions adopted and decentralized control laws designed are considerably simple comparing with many existing results. An example along with the respective numerical and computer simulation results is also given to demonstrate the effectiveness of the proposed consensus control synthesis.