Consensus Control Of Multi-agent Systems With A Dual-rate Sampling Mechanism

With the development of network,control and sensor technologies,the consensus control of multi-agent systems(MASs)has attracted the wide attention of researchers and made many remarkable achievements,due to its widespread applications in traffic control,power systems,robots,unmanned aerial vehicles,and military,etc.At the same time,many communication networks allow only digital information to spread.Then,sampling control strategies in MASs pave a feasible way to guarantee desired control performances and save limited computing and communication resources.However,the existing research results have a precondition in designing a control strategy,that is,it is assumed that all the measurement variables of each agent are sampled by some sensors with a same common sampling rate.It's worth pointing out that,due to the influence of sensors' processing time,response time,sampling accuracy,noise,etc.,the actual sampling rates of different types of sensors have their respective effective ranges.This leads to that different measurement variables of an MAS can be actually obtained by different sensors at different sampling rates,such the sampling way is called the multi-rate sampled-data(MRSD)mechanism.Due to its complexity,this thesis only considers one case of MRSD mechanism: dual-rate sampling,and mainly studies the consensus control of MASs with dual-rate sampling mechanism.The so-called dual-rate sampling mechanism means that the several measurement variables of each agent in the MASs are sampled by two groups of sensors at different sampling rates,and the sampling rates of each group are same.This thesis mainly studies the consensus control problem of MASs under a dual-rate sampling mechanism,and the detailed works are presented as follows:First,in order to better understand the dual-rate sampling mechanism,we consider the stabilization control of a single-agent system(i.e.,the system contains only one agent)with time delay inputs and uncertainties.The dual-rate sampling mechanism is used to state variables,and then a reasonable controller is designed to stabilize the system.By constructing a new Lyapunov function,a stability criterion is presented in terms of matrix inequalities,and also provides the relationship among the parameters such as the upper and lower bounds of both sampling intervals and time-delays.Two illustrative examples not only show the effectiveness and advantages of proposed control strategy,but also illustrate the advantages of the dual-rate sampling mechanism.Secondly,a dual-rate sampling mechanism is introduced in homogeneous MASs,and the state consensus problem is studied.By constructing dynamic compensators for each agent,a new distributed control strategy,which does not depend on the eigenvalue information of the Laplacian matrix,is proposed.Under the proposed control strategy,the state consensus problem of homogeneous MASs is solved by Lyapunov stability method.Moreover,each agent's controller design does not need continuous information of its neighbors.Then,two numeric examples are simulated for confirming the effectiveness of the theoretical results and the advantages of the dual-rate sampling mechanism.Finally,combined with the above research results,a dual-rate sampling mechanism is introduced in heterogeneous MASs,and the output consensus problem is investigated.By constructing observer and dynamic compensators for each agent,a distributed control strategy,that does not depend on the eigenvalue information of the Laplacian matrix,is proposed.Under the proposed control strategy,the output consensus problem of heterogeneous MASs can be solved by Lyapunov stability method.Moreover,each agent's controller design does not rely on continuous information of its neighbors.Then,a numeric simulation example is given to illustrate the effectiveness of our results and the advantages of the dual-rate sampling mechanism in improving the control performance of the MASs.