Consensus control of multi-agent systems is a representative problem in the field of multi-agent coordination, and is also the basis of many other decentralized control and estimation problems. As a novel interdisciplinary subject, the consensus control of multi-agent systems can explain many self-organization phenomena and swarm behaviors in the nature and human being society, and also has wide practical applications in the industry and national defence. So far most research on the multi-agent consensus problem has to assume that the agent has the deterministic first-order/second-order integrator dynamic model, however, strong nonlinearity, uncertainty and external disturbance are inevitable in the practical applications, and the consensus algorithm based on the simple agent dynamics cannot solve these problems. Therefore, the study on the consensus problem of multi-agent systems with complex dynamics has the significant meaning both in theory and in practice. On the basis of review and summary of the corresponding research on the multi-agent consensus problem, this thesis employs the matrix theory, adaptive control and neural networks as the major tools, studies the consensus problem of multi-agent systems with complex dynamics. The major contributions of this thesis are as follows: 1. An observer-based consensus algorithm is proposed for the multi-agent system with the general continuous-time time-invariant linear dynamics. The necessary and sufficient conditions on the consensus of the closed-loop linear multi-agent system are given, and specific controller design strategy is provided. The proposed algorithm does not need the complete state information of agents, and can guarantee all the agents' states can be convergent to a static common value. Finally, the obtained results are extended to the case where agents have the discrete-time time-invariant linear dynamics. 2. Under the assumption of ``linearity-in-parameters'', a decentralized adaptive consensus algorithm is proposed for a class of multi-agent systems with uncertain dynamics. Theoretical analysis shows the stability of the closed-loop multi-agent system. In addition, the proposed algorithm is extended to the case of agents with higher-order dynamics by the backstepping technique. At last, the effectiveness of proposed method is illustrated by the consensus control of multi-manipulator systems with uncertain kinematics and dynamics. 3. A neural-network-based robust adaptive algorith...
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