Finite-Time Distributed Aggregative Optimal Consensus of Multivehicle Systems With Multiple Time-Varying Constraints
研究了多车系统在多重时变约束下的有限时间分布式聚合最优一致性问题，提出了新的分布式算法，并考虑了时变未知控制增益和扰动，通过理论分析和仿真验证了算法有效性。
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.