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.

中文翻译：

---

应用于多车协作的多智能体系统分布式模型预测控制

本文提出了一种分布式模型预测控制 (DMPC) 方法，用于具有线性代理动力学和有界控制输入约束的多代理系统 (MAS) 的一致性控制。在提出的 DMPC 框架内，每个代理与邻居交换假设的状态轨迹并解决局部开环优化问题以获得最佳控制输入。在优化问题中，将离散时间共识协议引入到假设终端状态更新律设计中，严格证明了假设终端状态的渐近共识和递归可行性。与最优成本函数一起，在李雅普诺夫函数的设计中引入了无限系列的cost-to-go函数，最终证明了闭环渐近共识。