Kairui Chen

Adaptive consensus of nonlinear multi-agent systems with unknown backlash-like hysteresis

In this brief, we consider the consensus problem of high-order nonlinear multi-agent systems with unknown backlash-like hysteresis and unknown control direction. By using backstepping technique, a distributed adaptive control scheme, with the adoption of Nussbaum-type function, is developed to solve this consensus problem. Radial basis function neural network is employed to neutralize the uncertain nonlinear dynamics. The approximation error of the neural networks, together with external disturbance and a bounded term from the hysteresis model, is adaptively estimated and counteracted by a robust term. Under undirected connected communication topology, it is proved that the consensus can be achieved asymptotically with the proposed control protocol. Finally, a numerical example is presented to illustrate the performance of the proposed protocol. HighlightsUnknown backlash-like hysteresis and unknown control direction are considered.Dynamics of the systems are high-order with unknown nonlinear dynamics.A modified robust strategy is employed to deal with approximation error.

Coordination of multi-agent systems on interacting physical and communication topologies

Abstract A new framework is given for coordination of multi-agent systems that are interconnected by a physical coupling digraph. The edges of this graph represent physical couplings between agents that are fixed due to dynamical interactions. On top of the physical graph, distributed control protocols are designed where the allowed communications between agents for control purposes are prescribed by a second fixed communication digraph. The physical and communication digraphs are generally different and the combination of these two graphs forms a cyber-physical system. The interactions between physical and communication graphs are the focus of this paper. We consider different interactions between two graphs, including the case when their pinned Laplacian commutes, the case of the communication graph with diagonalizable pinned Laplacian, and the case of two general graphs. Moreover, within each graph, the relations between the agents can be either collaborative or antagonistic. To capture this, the theory of bipartite consensus is used. Coordination protocols for different cases are designed that are distributed with respect to the communication graph, and overcome the detrimental effects of the signed physical graph. The proposed control methods are illustrated by simulation examples.

Consensus of multi-agent nonlinear dynamic systems under slow switching topology

This paper is devoted to the consensus problem of multi-agent systems with inherent nonlinear dynamics. To reflect the practical environment of multi-agent systems, the communication topology is assumed to switch slowly within a finite set of directed topologies restricted by an average dwell time switching signal. By taking advantage of tools from switched systems and piecewise Lyapunov functions, it is shown that consensus can be guaranteed for the choice of suitable parameters if each topology contains a directed spanning tree. Explicit analytical conditions on both feedback gain and average dwell time of admissible switching signals for consensus are also offered. Finally, an example with computer simulation is provided to illustrate the theoretical results.

Fuzzy density weight-based support vector regression for image denoising

Support vector machine (SVM) is a popular machine learning technique and its variant least squares support vector regression (LS-SVR) is effective for image denoising. However, conventional LS-SVR does not fully consider the sampling distribution of noisy images, which may degrade the performance of the algorithm. In this paper, we propose a new fuzzy density weight SVR (FDW-SVR) denoising algorithm, which assigns fuzzy priority to each sample according to its density weight. FDW is designed to estimate the joint probability density function via the fuzzy theory based on the pixel density and neighborhood density. Extensive experimental results show that FDW-SVR is superior to those state-of-the-art denoising techniques in light of both subjective and objective evaluations.

Consensus of High-Order Nonlinear Multiagent Systems with Constrained Switching Topologies

The relationship between control and communication constraints is becoming of central importance in the consensus problem of networked agents. In this paper, we investigate such a problem for nonlinear multiagent systems with Lipschitz dynamics. To reflect communication constraints, the topology is assumed to switch within a finite set of digraphs characterised by an average dwell time switching signal. By constructing a suitable multiple Lyapunov function, we show that consensus can be reached under the designed consensus protocol. A multistep algorithm for designing consensus protocol is then developed by solving the Lyapunov equation and algebraic Riccati equation. Finally, simulation examples are delineated to substantiate the effectiveness of the proposed algorithms.

Distributed Consensus of Nonlinear Multi-Agent Systems on State-Controlled Switching Topologies

This paper considers the consensus problem of nonlinear multi-agent systems under switching directed topologies. Specifically, the dynamics of each agent incorporates an intrinsic nonlinear term and the interaction topology may not contain a spanning tree at any time. By designing a state-controlled switching law, we show that the multi-agent system with the neighbor-based protocol can achieve consensus if the switching topologies jointly contain a spanning tree. Moreover, an easily manageable algebraic criterion is deduced to unravel the underlying mechanisms in reaching consensus. Finally, a numerical example is exploited to illustrate the effectiveness of the developed theoretical results.

Cluster consensus of multi-agent systems with heterogeneous dynamics

This paper studies the cluster consensus problem of heterogeneous linear multi-agent systems under fixed directed topology. Compared to existing results for cluster consensus problems, dynamics of all agents are allowed to be nonidentical, which promotes the application of the results. A state-feedback control protocol with a dynamic compensator is proposed to solve the problem. Output regulation technique is introduced to prove that the proposed control protocol is effective. A numerical simulations is provided to verify the effectiveness of the theoretical results.

Adaptive leader-following consensus of nonlinear multi-agent systems with jointly connected topology

This paper studies the leader-following consensus problem of general multi-agent systems, in which dynamics of all agents are affected by unknown nonlinear dynamics. By parameterizing the unknown nonlinear terms, a distributed adaptive consensus algorithm is proposed for each follower agent. With a modified labeling rule, consensus analysis is conducted for the multi-agent systems with jointly connected topology based on algebraic graph theory and Lyapunov theory. Finally, a simulation example is provided to validate the theoretical results.