Research On Model Predictive Control Of Networked Multi-agent System

Networked multi-agent system is a kind of system which is composed of several intelligences which can communicate and cooperate with each other.It plays an important role in the research of intelligent city,intelligent transportation,sensor network and other fields.Due to the existence of the network,the network induced delay,packet loss,communication channel congestion and other problems will inevitably exist in the networked multi-agent system,and the multi-agent system itself has a certain complexity,so we consider the use of distributed control algorithm for processing.Model predictive control algorithm has attracted much attention because of its good optimization control effect and ability to deal with various constraints,but its iterative optimization characteristics have also become its biggest short board,repeated iterations will bring a great computational burden to the computer.Therefore,this paper studies the model predictive control problem of networked multi-agent system based on event-triggered mechanism,stochastic communication protocol and contingency terminal cost function.The main findings are summarized below:Firstly,event-triggered active model predictive control is investigated for a nonlinear multi-agent system with packet losses.By designing event-triggered mechanisms which reduce sensing cost,event-triggered conditions are detected at certain sampling instants.Prediction horizons of all agents are selected actively through the event-triggered mechanisms.Selecting a maximal predictive horizon,a common predictive horizon of the nonlinear multi-agent system is obtained to ensure synchronous updating.Bernoulli distributions are applied to describe the packet losses which are deal with by model predictive control.Finally,effectiveness of the proposed algorithm design is verified by a numerical simulation.Secondly,the consistency of multi-agent systems with network-induced delay and stochastic communication protocols is investigated by model predictive control.By designing a communication waiting mechanism,each agent has a certain tolerance to the delay.while the tolerance is determined by the conditions that ensure the stability of thesystem.At the same time,a random communication protocol is designed for multi-agent system,which ensures the orderly communication of multi-agent system.Finally,effectiveness of the proposed algorithm design is verified by a numerical simulation.Nonlinear model predictive control is investigated for a nonlinear system using a time-varying terminal cost function.An artificial reference is added as a decision variable for tracking an unreachable periodic reference.Some constraints are given in an optimization problem to guarantee feasibility if the periodic references change suddenly for the reason of that the constraints are independent on the periodic references.A time-varying periodic polytopic linear difference inclusion is proposed to guarantee behavior locally around the artificial reference of the nonlinear system.A time-varying weight matrix is designed for the time-varying terminal cost function to ensure stability of the nonlinear system.Effectiveness of the developed tracking control scheme is shown by a simulation example on a mobile robot system.