Finite-Time Distributed Aggregative Optimal Consensus of Multivehicle Systems With Multiple Time-Varying Constraints

In this article, the finite-time distributed aggregative optimal consensus (DAOC) problems for multivehicle systems (MVSs) with multiple time-varying constraints are investigated. First, we formulate a new distributed optimization model, called finite-time DAOC (FT-DAOC), where each cost function contains an extra aggregative variable. Then, a class of new finite-time distributed algorithms is designed for MVSs with time-varying cost functions under time-varying digraphs. Moreover, as vehicles may work in settings with time-varying unknown control gains and unknown disturbances, we extend the newly presented distributed algorithms to solve finite-time aggregative optimal consensus issues for MVSs with multiple time-varying constraints. Finally, the validity of the newly illustrated algorithms is analyzed theoretically, and two simulation examples are provided.