Event-triggered Consensus Tracking Control For Uncertain Nonlinear Nonstrict-feedback Multi-agent Systems

In recent years,multi-agent systems（MASs）have received more and more attention from scholars and have become a current hot research topic in artificial intelligence as well as in control engineering.The relevant results have achieved significant application effectiveness in the practical systems,such as physical-biological systems,sensor networks and robot teaming^[1-3].It is well known that the practical MASs can be modeled accurately by the nonlinear nonstrict-feedback systems than the linear systems.Moreover,the unknown uncertainties are commonly found in various control processes of the industrial system,and these unknown uncertainties can usually destroy the performance of the system or even lead to system instability.Therefore,it is of great theoretical significance and social value to design the effective control strategies for nonlinear nonstrict-feedback MASs with unknown uncertainty.It should be noted that most of the control strategies proposed for nonlinear MASs are traditional time-triggered control（TTC）strategies,which will cause unnecessary waste of communication resources.Compared with the TTC strategy,the event-triggered control（ETC）strategy,as an important technical method in the modern information technology industry,has incomparable advantages in terms of saving computation and communication resources.In this thesis,based on the system stability theory of Lyapunov and the knowledge of graph theory,we combine Backstepping method and neural network（NN）technique to study the consensus control problem of several types of nonlinear nonstrict-feedback MASs with unknown uncertainty based on ETC scheme.The specific work is as follows:（1）This thesis proposes the event-triggered adaptive asymptotic tracking control approach for a class of nonlinear nonstrict-feedback MASs with unknown control gains.First of all,in order to satisfy the requirement that all items in the controller must be available,the unknown nonlinearities in the system are flexibly approximated by utilizing radial basis function neural networks（RBF NNs）technique.Moreover,the issue of“complexity explosion”in the backstepping procedure is handled by improving the traditional dynamic surface control（DSC）technology,and meanwhile the influences of the boundary layers caused by the filters in the DSC procedure are eliminated skillfully through the compensation terms.In addition,the relative threshold ETC strategy is developed for the obtained controllers to reduce the waste of communication resources,where Zeno phenomenon is successfully avoided.It is observed that the new presented control strategy ensures that all the closed-loop systems variables are uniformly ultimately bounded（UUB）,and furthermore all the outputs of followers are able to track the output of the leader with zero tracking errors.Finally,a simulation result is presented to show the effectiveness of the obtained design scheme.（2）This thesis proposes the event-triggered-based bipartite finite-time consensus tracking control approach for a class of nonlinear nonstrict-feedback MASs with the unknown time-varying disturbances.First,the major design difficulties generated by the entirely unknown nonlinear functions containing all states are solved by utilizing the approximation property of RBF NNs and the structural feature of Gaussian functions.Then in the backstepping procedure,the issue of“explosion of complexity”is handled by combining the adaptive neural approach and the command filter technique.Meanwhile,the novel compensation signals are designed,which skillfully eliminate the error influence caused by the filters.Moreover,to save the communication resources,the relative threshold ETC scheme is presented for the designed controllers,and there is no Zeno phenomenon.Overall,it is shown that the new proposed control approach drives the tracking errors dichotomously to the desired neighborhood of the origin in finite time,and all the signals in the closed-loop systems are finite-time bounded.Finally,two simulation results are both given to show the validity of the obtained design method.In conclusion,the problem of consensus tracking control for nonlinear nonstrict-feedback MASs with unknown uncertainty is studied based on ETC scheme,and two effective control strategies are proposed simultaneously in this thesis.There are still many control issues that need to be further explored for such systems,such as the optimal control problem of nonlinear MASs and the network security protection problem.