Discrete-time Nonlinear Multi-agent Systems Research Articles

Decentralized adaptive synchronization with bounded identification errors for discrete-time nonlinear multi-agent systems with unknown parameters and unknown high-frequency gains

This paper addresses the challenging problem of decentralized adaptive control for a class of coupled hidden leader-follower multi-agent systems, in which each agent is described by a nonlinearly parameterized uncertain model in discrete time and can interact with its neighbors via the history information from its neighbors. One of the agents is a leader, who knows the desired reference trajectory, while other agents cannot receive the desired reference signal or are unaware of existence of the leader. In order to tackle unknown internal parameters and unknown high-frequency gains, a projection-type parameter estimation algorithm is proposed. Based on the certainty equivalence principle and neighborhood history information, the decentralized adaptive control is designed, under which, the boundedness of identification error is guaranteed with the help of the Lyapunov theory. Under some conditions, it is shown that the multi-agent system eventually achieves synchronization in the presence of strong couplings. Finally, a simulation example is given to support the results of the proposed scheme.

Formation Control of Heterogeneous Discrete-Time Nonlinear Multi-Agent Systems With Uncertainties

This paper investigates the formation control problem of heterogeneous discrete-time nonlinear multi-agent systems with structural uncertainties. The control objective is to make the outputs of agents reach a desired formation while tracking a leader. The formation control problem of heterogeneous discrete-time multi-agent systems is first converted into a robust output regulation problem, and then, a distributed formation control scheme based on output regulation framework is proposed. A $p$ -copy internal model is synthesized into the distributed formation control law such that the overall closed-loop system can be robust to structural uncertainties. It is shown that the heterogeneous discrete-time nonlinear multi-agent system can reach the desired formation regardless of small parameter permutations of agents under the proposed control law. Finally, simulation and experiment studies are provided to demonstrate the effectiveness of the proposed control law.

Leader-following consensus of nonlinear discrete-time multi-agent systems with limited communication channel capacity

This paper considers the problem of the leader-following consensus of generally nonlinear discrete-time multi-agent systems with limited communication channel capacity over directed fixed communication networks. The leader agent and all follower agents are with multi-dimensional nonlinear dynamics. We propose a novel kind of consensus algorithm for each follower agent based on dynamic encoding and decoding algorithms and conduct a rigorous analysis for consensus convergence. It is proved that under the consensus algorithm designed, the leader-following consensus is achievable and the quantizers equipped for the multi-agent systems can never be saturated. Furthermore, we give the explicit forms of the data transmission rate for the connected communication channel. By properly designing the system parameters according to restriction conditions, we can ensure the consensus and communication efficiency with merely one bit information exchanging between each pair of adjacent agents per step. Finally, simulation example is presented to verify the validity of results obtained.

Consensus of a class of discrete-time nonlinear multi-agent systems in the presence of communication delays

In this paper, we study the consensus problem for a class of discrete-time nonlinear multi-agent systems. The dynamics of each agent is input affine and the agents are connected through a connected undirected communication network. Distributed control laws are proposed and consensus analysis is conducted both in the absence and in the presence of communication delays. Both theoretical analysis and numerical simulation show that our control laws ensure state consensus of the multi-agent system.

Consensus-based distributed cooperative learning control for a group of discrete-time nonlinear multi-agent systems using neural networks

This paper focuses on the cooperative learning capability of radial basis function neural networks in adaptive neural controllers for a group of uncertain discrete-time nonlinear systems where system structures are identical but reference signals are different. By constructing an interconnection topology among learning laws of NN weights in order to share their learned knowledge on-line, a novel adaptive NN control scheme, called distributed cooperative learning control scheme, is proposed. It is guaranteed that if the interconnection topology is undirected and connected, all closed-loop signals are uniform ultimate bounded and tracking errors of all systems can converge to a small neighborhood around the origin. Moreover, it is proved that all estimated NN weights converge to a small neighborhood of their common optimal value along the union of all state trajectories, which means that the estimated NN weights reach consensus with a small consensus error. Thus, all learned NN models have the better generalization capability than ones obtained by the deterministic learning method. The learned knowledge is also adopted to control a class of uncertain systems with the same structure but different reference signals. Finally, a simulation example is provided to verify the effectiveness and advantages of the distributed cooperative learning control scheme developed in this paper.