Event-triggered Prescribed Performance Intelligent Adaptive Control Of Nonlinear Multi-agent Systems

The multi-agent system is a system composed of multiple agents that extract information from the environment and make management decisions based on that information.Due to their wide range of applications,including mobile robots,drones,and smart grids,multi-agent systems have attracted a lot of attention.Most early work on multi-agent systems was based on linear system models.However,in practice,the widespread presence of nonlinear and unknown disturbances raises higher demands on the system model.To address this issue,we use nonlinear non-strict feedback systems to model practical multi-agent systems.For control problems in systems with unknown nonlinearities,transient and steady-state performance are issues that must be considered.However,most current research does not focus on transient performance,which prompts us to conduct research on predetermined performance control.Moreover,compared with other control systems,multi-agent systems require more communication in practical applications.However,communication resources of agents are limited,which prompts us to study communication mechanisms.Therefore,reducing communication burden while providing transient system performance is an important issue in multi-agent system research.To address this issue,this work introduces prescribed performance control and eventtriggered communication mechanisms into the control framework of nonlinear multi-agent systems with non-strict feedback,and proposes a new distributed coordination control strategy based on adaptive backstepping control.The main contributions of this paper are as follows:(1)Introducing prescribed performance control into the control framework of intelligent adaptive control of nonlinear multi-agent systems with non-strict feedback.Unlike previous research,the system studied in this paper is a non-strict feedback nonlinear multi-agent system,which is more realistic.In addition,this paper changes the form of the conversion error and designs a new neural network adaptive controller to make the conversion error smooth and weaken the chattering problem.Then,by using a new Lyapunov function with a lower bound on the control coefficient and a boundary estimation method,the requirement for an upper limit on the control gain function in stability analysis is eliminated.This allows us to achieve asymptotic tracking control of non-strict feedback systems by constructing smooth functions and using boundary estimation.A consistent algorithm for performance guarantee is provided to ensure system steadystate and transient performance.Simulation results show that compared to other control strategies,the introduction of predetermined performance control ensures both system steady-state and transient performance and paves the way for further research.(2)Intelligent adaptive control of nonlinear multi-agent systems with specified performance and event-triggered communication.To address the communication burden in non-strict feedback multi-agent systems,event-triggered mechanisms are introduced into the control framework of intelligent adaptive control of nonlinear multi-agent systems with specified performance.Unlike previous research,the system studied in this paper is a non-strict feedback nonlinear multi-agent system,which is more realistic.A new event-triggered adaptive law is proposed,which can not only solve the algebraic loop problem caused by non-strict feedback systems but also effectively save communication resources while avoiding the Zeno effect.Compared with other control strategies,simulation results show that,the introduction of event-triggered mechanisms could effectively optimize the use of communication resources.