Showing papers by "Yurong Liu published in 2017"

Consensus control of stochastic multi-agent systems: a survey

Abstract: In this article, we provide a review of the consensus controlproblem for stochastic multi-agent systems (MASs). Recent advancesare surveyed according to the method of occurrence of the stochasticity of the MASs. First, the consensus problem is discussed for MASs, whereinindividual agents are corrupted by random noises, i.e., thedynamics of agents involve stochasticity in process and/ormeasurement equations. Both additive noises and multiplicativenoises are surveyed in detail and special attention is paid to theMASs whose dynamics are governed by Itodifferential equations.Moreover, particular effort is devoted to presenting the latestprogress on the consensus problem for a special type of stochasticMAS with Markovian jump parameters. Subsequently, the relevant research issummarized for MASs with noisy communication environments andstochastic sampling. Further, we provide a systematic review ofthe consensus problems for MASs whose communication topologyvaries randomly in the process of data propagation among agents.Finally, conclusions are drawn and several potential future researchdirections are outlined.

State-feedback controller design for disturbance decoupling of Boolean control networks

Abstract: This paper investigates the disturbance decoupling problem for Boolean control networks. Firstly, using the matrix semi-tensor product, the logical dynamic equations of Boolean control networks are converted into discrete time linear equations. Secondly, the solvability of the disturbance decoupling problem is analyzed based on the structure matrices of BCNs, and a method for design state-feedback controllers is put forward to deal with this problem. Two necessary and sufficient conditions for the disturbance decoupling problem of BCNs are obtained. Then an algorithm is provided to design controllers for calculating the disturbance decoupling forms of Boolean control networks. Finally, an illustrative example is given to describe the proposed algorithm.

Genetic algorithm-based compliant robot path planning: an improved Bi-RRT-based initialization method

Abstract: Purpose The purpose of this paper is to improve the performance of the genetic algorithm-based compliant robot path planning (GACRPP) in complex dynamic environment by proposing an improved bidirectional rapidly exploring random tree (Bi-RRT)-based population initialization method. Design/methodology/approach To achieve GACRPP in complex dynamic environment with high performance, an improved Bi-RRT-based population initialization method is proposed. First, the grid model is adopted to preprocess the working space of mobile robot. Second, an improved Bi-RRT is proposed to create multi-cluster connections between the starting point and the goal point. Third, the backtracking method is used to generate the initial population based on the multi-cluster connections generated by the improved Bi-RRT. Subsequently, some comparative experiments are implemented where the performances of the improved Bi-RRT-based population initialization method are compared with other population initialization methods, and the comparison results of the improved genetic algorithm (IGA) combining with the different population initialization methods are shown. Finally, the optimal path is further smoothed with the help of the technique of quadratic B-spline curves. Findings It is shown in the experiment results that the improved Bi-RRT-based population initialization method is capable of deriving a more diversified initial population with less execution time and the IGA combining with the proposed population initialization method outperforms the one with other population initialization methods in terms of the length of optimal path and the execution time. Originality/value In this paper, the Bi-RRT is introduced as a population initialization method into the GACRPP problem. An improved Bi-RRT is proposed for the purpose of increasing the diversity of initial population. To characterize the diversity of initial population, a new notion of breadth is defined in terms of Hausdorff distance between different paths.

Distributed sampled-data containment control of linear multi-agent systems with fixed topology

Abstract: This study investigates the sampled-data containment control problem for linear multi-agent systems with multiple leaders under undirected or directed network topology. First, by means of the decomposition method, the containment control problem is transformed into the simultaneous stability analysis issue of certain subsystems. Then, the solution of a class of differential equations, which plays a key role for the addressed problem, is exploited by using the contradiction method. Together with a novel Lyapunov function, sufficient conditions are established to guarantee that all the followers move into the convex hull spanned by the leaders. The proposed results are dependent on the information of the system dynamics, the coupling strength as well as the eigenvalues of topology matrix. Furthermore, the established results are specialised to both the leader–following case and the traditional consensus case. Finally, simulations are given to illustrate the theoretical results derived in this study.