论文标题
分布式模型预测了对多机车合作应用的多代理系统的控制
Distributed Model Predicted Control of Multi-agent Systems with Applications to Multi-vehicle Cooperation
论文作者
论文摘要
本文提出了一种分布式模型预测控制方法（DMPC）方法，以通过线性代理动力学和有界控制输入约束对多代理系统（MASS）共识控制。在提出的DMPC框架内，每个代理都会交换与邻居的状态轨迹，并解决了局部开放环优化问题，以获得最佳控制输入。在优化问题中，将离散的时间共识方案引入了假定末端状态的更新法律设计中，该法律设计与假定的末端状态的渐近共识和递归可行性得到了严格证明。加上最佳成本函数，将一系列无限的成本函数引入到Lyapunov函数的设计中，最终证明了闭环渐近共识。使用两种申请，包括自动水下车辆（AUV）以及连接和自动化车辆（CAVS）的合作来验证拟议的DMPC方法的有效性。
This paper proposes a distributed model predicted control (DMPC) approach for consensus control of multi-agent systems (MASs) with linear agent dynamics and bounded control input constraints. Within the proposed DMPC framework, each agent exchanges assumed state trajectories with neighbors and solves a local open-loop optimization problem to obtain the optimal control input. In the optimization problem, a discrete-time consensus protocol is introduced into update law design for assumed terminal states, with which asymptotic consensus of assumed terminal states and recursive feasibility are rigorously proved. Together with the optimal cost function, an infinite series of cost-to-go functions is introduced into the design of a Lyapunov function, with which closed-loop asymptotic consensus is finally proved. Two applications including cooperation of autonomous underwater vehicles (AUVs) and connected and automated vehicles (CAVs) are used to validate the effectiveness of the proposed DMPC approach.
