Study On Consensus Of Distributed Multi-agent System

In recent years, with the rapid development of distributed multi-agent systems, distributed cooperative control research becomes a hot spot in the field of control science research. The cooperative control of multi-agents investigates how a large number of individuals with simple function can perform complicated tasks or achieve cooperative group behaviors by distributed control. As the basis of the cooperative control of multi-agents, consensus problem attracts much attention from many different fields. In a multi-agent system, consensus means that some of the agents'states reach at the same values, and consensus algorithm is a protocol for agents reach consensus based on local information. This thesis focuses on the consensus algorithm with state predictor; the state predictor strategy applied to the multi-agent formation and coverage problems; the impact of communication delay on group convergence; the sufficient condition of convergence of consensus algorithm with input saturation.The main contributions of this dissertation are summarized as follows: 1. Study on a consensus algorithm with a state predictor. In order to improve the performance of a consensus algorithm, this thesis presents a state predictor strategy, and under this strategy, the smallest non-zero eigenvalue of Laplacian Matrix can be increased. As a result, the performance is improved greatly. The state predictor strategy can also be applied to the second-order multi-agent system consensus algorithm. Simulations are introduced to show that state predictor is effective to raise the speed of the system to complete the task.2. The performance analysis of formation and coverage algorithm with state predictor strategy. A distributed multi-agent system's formation algorithm with the state predictor algorithm is introduced to improve the performance of the distributed multi-agent system to complete the formation task; and a distributed multi-agent systems coverage algorithm with the state predictor strategy is also introduced to improve the performance of the distributed multi-agent system to complete the coverage task. Simulations are given to verify the effectiveness of the state predictor strategy.3. Study on effects of communication delay to the evolution of multi-agent system. If the communication delay is too large, multi-agent systems maybe fail to achieve consensus, and the robustness with respect to communication delay of Multi-agent system with state predictor is weaker than that of Multi-agent system without state predictor. Furthermore, the maximum communication delay is studied.4. Analysis of Multi-agent system consensus algorithm with input saturation. When consensus can be achieved without input constraints, a sufficient condition that the multi-agent system still achieving consensus with respect to input saturation is presented.