6533b825fe1ef96bd128287e

RESEARCH PRODUCT

Distributed adaptive leader–follower and leaderless consensus control of a class of strict-feedback nonlinear systems : a unified approach

Wei WangJing ZhouChangyun WenJiangshuai HuangGuoqi Li

subject

0209 industrial biotechnologyClass (computer programming)Computer science020208 electrical & electronic engineeringParameterized complexity02 engineering and technologyComputer Science::Multiagent SystemsVariable (computer science)Nonlinear systemAdaptive Control020901 industrial engineering & automationControl and Systems EngineeringControl theory0202 electrical engineering electronic engineering information engineeringTrajectory:Electrical and electronic engineering [Engineering]Uniform boundednessElectrical and Electronic EngineeringSpecial caseDistributed Consensus Control

description

In this paper, distributed adaptive consensus for a class of strict-feedback nonlinear systems under directed topology condition is investigated. Both leader–follower and leaderless cases are considered in a unified framework. To design distributed controller for each subsystem, a local compensatory variable is generated based on the signals collected from its neighbors. Such a technique enables us to solve the leader–follower consensus and leaderless consensus problems in a unified framework. And it further allows us to treat the leaderless consensus as a special case of the leader–follower consensus. For leader–follower consensus, the assumption that the leader trajectory is linearly parameterized with some known functions as required in most recent relevant literatures is successfully relaxed. It is shown that global uniform boundedness of all closed-loop signals and asymptotically output consensus could be achieved for both cases. Simulation results are provided to verify the effectiveness of our schemes. This work was supported by National Key Research and Development Program of China under grants no. 2019YFB1312002, 2018AAA0101100, and National Natural Science Foundation of China under Grants 61973017, 61673035, and 61703061.

https://hdl.handle.net/11250/2735030