约束异构多智能体系统最优一致性的分布式模型预测控制
Distributed Model Predictive Control for Optimal Consensus of Constrained Heterogeneous Multi-agent Systems
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中文总结 AI 辅助
本文针对约束异构多智能体系统，在MPC框架下同时优化控制输入序列与一致性平衡点，开发分布式原始-对偶算法并建立终端条件，经数值仿真验证了所提分布式最优一致性控制方法的有效性。
中文摘要 AI 辅助
本文在模型预测控制（MPC）框架下研究约束异构多智能体系统的分布式最优一致性控制问题，在提出的MPC框架中同时优化控制输入序列与动态可行的一致性平衡点以提升一致性性能，在每个预测时刻产生耦合的约束优化问题。本文开发分布式原始-对偶算法求解该优化问题，并推导可局部验证的条件以保证其收敛性；进一步为所提MPC框架建立充分终端条件，保证闭环异构多智能体系统的递归可行性与渐近一致性；最后通过数值仿真验证所提方法的有效性。
英文摘要
This paper investigates the distributed optimal consensus control problem of constrained heterogeneous multi-agent systems within a model predictive control (MPC) scheme. Both the control input sequence and the dynamically feasible consensus equilibrium are optimized simultaneously within the proposed MPC framework to improve consensus performance, yielding a coupled constrained optimization problem at each prediction time. A distributed primal--dual algorithm is developed to solve the resulting optimization problem, and locally verifiable conditions are derived to guarantee its convergence. Furthermore, sufficient terminal conditions are established for the proposed MPC framework to guarantee the recursive feasibility and asymptotic consensus of the closed-loop heterogeneous multi-agent systems. Finally, numerical simulations verify the effectiveness of the proposed approach.
