Cluster Consensus For Unknown Nonlinear Multi-Agent Systems

With the rapid development of computer and communication industry in recent years,it has been widely applied in many practical scenarios.Most of the previous researches focus on the consensus control of multi-agent systems.With the deepening of the research,people gradually pay attention to a more general research topic ——cluster consensus control.Multiple agents are divided into different subgroups,and the consensus of a certain state or output within the same subgroup enables the system to complete more complex tasks.Although this research has achieved certain results,there are still unresolved issues.This paper mainly studies the problem of cluster consensus of nonlinear multi-agent systems with unknown dynamics.The main contents are as follows:(1)This paper studies cluster consensus problem for a class of unknown nonlinear multi-agent systems with directed communication topology.Firstly,a distributed continuous neural network(NN)-based adaptive protocol is presented for solving this problem by introducing reference model to each agent.Then,taking limited communication resource and energy consumption into account,a distributed eventtriggered cluster consensus protocol is proposed.Different from the existing results,two event-triggered mechanisms are constructed in the proposed event-triggered protocol to reduce communication load and control update frequency as possible.The sufficient conditions that guarantee cluster quasi-consensus under the both proposed protocols are obtained,respectively.Zeno behavior is proved to be excluded.Finally,simulation results verify the effectiveness of the proposed protocols.(2)This paper studies cluster consensus problem for non-affine nonlinear multiagent systems with actuator faults in directed communication topology.Due to the complexity of the dynamics of non-affine systems and the existence of actuator faults,the neural network approach to the system model is not suitable.Firstly,it is proved that there is an ideal controller which satisfies a certain linear form of the system dynamics,and then an artificial neural network is used to approximate the ideal controller online,and the updating rate of the weights of the artificial neural network is designed according to Lyapunov theorem.Secondly,in order to make the agent reach the desired trajectory,the containment control is introduced.Finally,the effectiveness of the proposed algorithm is verified by numerical simulation under normal and faulty actuators,respectively.