Prescribed Finite-Time Consensus Tracking for Multiagent Systems With Nonholonomic Chained-Form Dynamics

This paper addresses the event-triggered consensus tracking for general linear multi-agent systems with nonlinear uncertainty dynamics under directed communication topology. The proposed event-triggered consensus strategy relaxes the assumptions in existing works that the control gain and local inputs must be continuous. With the consensus tracking condition being satisfied, the cooperative control law with bounded parameter is developed based on the intermittent samples. It shows that if the sampling condition, in terms of LMIs, is satisfied, the resulting closed-loop general linear multi-agent uncertain systems will achieve the consensus. Simulation results are provided to verify the proposed approach.

This paper deals with the finite-time and fixed-time bipartite consensus tracking (FFBCT) problems for multi-agent systems (MASs) , in which both cooperative and competition exist. Using Lyapunov stability methods, distributed nonlinear protocols are provided to ensure all agents reach agreement. Its absolute value is the same but it might be different in direction. Besides, the second protocol can make the system reach bipartite consensus in fixed time regardless of the initial state of agents. Moreover, boundaries for settling times are estimated. Some numerical examples are finally presented to prove the validity of our designs.

This paper is concerned with the problem of bipartite consensus tracking for multi-agent systems with both cooperative and antagonistic interactions. These interactions among vertices in a signed digraph are first investigated with the signs of the weighted products of associated paths in their augmented undirected graph. To improve the convergence rate, a finite-time protocol and a fixed-time protocol are developed to solve the bipartite consensus tracking problem with a finite-time and a settling time independent of any initial states, respectively. Moreover, an M-matrix based method is well proposed to estimate all parameters in the constructed Lyapunov function, therefore, the upper bounds of the settling times for finite-time and fixed-time cases can be analytically determined. Finally, numerical examples are provided to demonstrate the effectiveness of proposed methods.

In this article, the asymptotic tracking consensus problem of higher-order multiagent systems (MASs) with general directed communication graphs is addressed via designing event-triggered control strategies. One common assumption utilized in most existing results on such tracking consensus problem that the inherent dynamics of the leader are the same as those of the followers is removed in this article. In particular, two cases that the dynamics of the leader are subjected, respectively, to bounded input and unknown nonlinearity are considered. To do this, distributed event-triggered observers are first constructed to estimate the state information of the leader. Then, local event-triggered tracking control protocols are designed for each follower to complete the goal of tracking consensus. One distinguishing feature of the present distributed observers lies in the fact that they could avoid the continuous monitoring for the states of the neighbors' observer states. It is also worth pointing out that the present tracking consensus control strategies are fully distributed as no global information related to the directed communication graph is involved in designing the strategies. Two simulation examples are finally presented to verify the efficiency of the theoretical results.

This paper studies the consensus tracking problem of a class of multi-agent systems where the dynamics of the leader and the followers are described respectively by integer and fractional order differential equations. It is shown that leader-following consensus tracking can be ensured if some carefully selected followers are informed and the relative position-based protocols are appropriately designed. A necessary and sufficient condition for the leader-following consensus in multi-agent systems is first proposed. It is found that, the real and imaginary parts of the eigenvalues of the augmented Laplacian matrix of the topology play important roles in achieving consensus. Finally, some numerical simulations are given to verify the effectiveness of the theoretical results.

This brief considers the leader-following consensus (consensus tracking) problem of multi-agent systems (MAS) with event-triggered communication. A novel hybrid event-triggering mechanism is proposed to reduce the burden of network transmission, and it is ensured that Zeno behavior does not occur. Besides, it is required that the control protocol is only updated at agent’s own triggering instant. Compared with the existing works, the main contribution is that the minimum inter-event time (MIET) can be pre-specified, which is important for physical implementation, and the control update frequency is also reduced. An example is given to verify the correctness of the proposed hybrid event-triggered control strategy.

This paper studies the adaptive consensus tracking problem for multi-agent systems modeled as high-order integrators with uncertain nonlinear dynamics under directed communication graphs. By parameterizing the unknown dynamics of the agents and the unknown input of the leader, two decentralized tracking control laws are designed using the state information and only the output information, respectively. With state information, globally uniformly ultimately bounded consensus tracking with arbitrarily small tracking errors can be achieved and when only output information is available, based on high-gain observers, semi-global result can be obtained. Numerical examples are provided to illustrate the effectiveness of the controllers.

This paper investigates the consensus tracking problem for the discrete multi-agent system (DMAS) with time-delay. By using iterative learning control (ILC) method and convergence in discrete system, the convergence properties for total system can be analyzed based on the time-domain analysis, frequency-domain analysis and matrix theory. The result shows that the proposed methods depend on each agent's self-delay time, the weights of the edges to each agent's neighbors, and the interconnection topology of the network. The iterative number of learning control relies on the sampling period and self-delay time for the discrete multi-agent system. Finally, numerical examples are presented to demonstrate the theoretical results.

Asymptotic bounded consensus tracking of multi-agent systems with a bounded-acceleration target via sampled-data control

In this paper, the consensus tracking problem for nonlinear multi-agent systems subjected to the output constraint, data loss and switching topologies is considered. Firstly, a consensus term is defined based on the communication information in present of the nonlinear output saturation and time-varying switching topologies. Since the random data loss is taken into consideration, the consensus term is redesigned by an introducing stochastic variable, which obeys the Bernoulli sequence with known probability. Then, a novel distributed ILC algorithm is designed by using the incomplete communication data of agents, which is more universal than the results without considered nonlinear constraint and random factors simultaneously. Through the contraction mapping method, an obtained convergence condition can also guarantee the asymptotic convergence of the agent along the iteration axis under nonlinear saturation factor and random factors. It is verified that the proposed algorithm can handle more complex situations in the consensus control of multi-agent systems. Simulation examples are further provided to verify the effectiveness of the proposed algorithm.

Adaptive MLP neural network controller for consensus tracking of Multi-Agent systems with application to synchronous generators

This paper investigates the fixed-time-synchronized consensus tracking problem for multi-agent systems with undirected topology. A nonlinear distributed control law for first-order multi-agent systems is proposed based on norm-normalized sign function, with which all the elements of all the agents' states track the leader's state at the same time and in fixed time. Then, the corresponding results are extended to second-order multi-agent systems by adopting the terminal sliding mode control method. Finally, numerical simulations are performed to verify the theoretical results in this paper.

In this paper, consensus tracking for a class of second-order nonlinear multi-agent systems with a dynamic leader under general directed communication graphs is considered. Only relative output measurements are assumed to be available for each agent. Based on a distributed observer design, global consensus tracking controller using only relative output information is proposed. Sufficient conditions on the controller parameters are derived to ensure consensus tracking. A simulation example is provided to illustrate the effectiveness of the proposed controller.

In the technical note, consensus tracking is studied for multi-agents system (MAS) with state time-delays. Every agent of the MAS has homogeneous dynamic function, which is described by a nonlinearly parameterized functions with unknown state time-delays. Moreover, it is assumed that the virtual leader (regarded as the desired trajectory) can be accessible to at least one agent. Based on several necessary assumptions, a distributed adaptive ILC is designed. In order to prove the convergence of tracking error, a composite energy function is designed. The result of convergence analysis shows that the designed control strategy can guarantee the agents track virtual leader along the iteration axis. A numerical simulation is provided to illustrate the effectiveness of the adaptive iterative learning controller.

This article addresses the distributed consensus tracking of multiagent systems (MASs) under hybrid attacks, which are composed of denial-of-service (DoS) attacks and continuously occurring false-data-injection (FDI) attacks. To avoid the overuse of communication resources, a dynamic event-triggered condition is established with good flexibility to determine the necessary triggering instants. Based on this, an adaptive law for updating the weighting matrix for the neural network (NN) approximator is designed, where only relative state variables at triggered instants are employed. By introducing a proper projection operator in the NN updating law, the uniform ultimate boundedness (UUB) of the variables contained is guaranteed.