通讯信息约束下具有全局稳定性的分布式系统预测控制（英文）

发布时间：2018-04-17 11:22

本文选题：大规模系统 + 预测控制　；参考：《控制理论与应用》2017年05期

【摘要】：本文针对一类由状态相互耦合的子系统组成的分布式系统,提出了一种可以处理输入约束的保证稳定性的非迭代协调分布式预测控制方法(distributed model predictive control,DMPC).该方法中,每个控制器在求解控制率时只与其它控制器通信一次来满足系统对通信负荷限制;同时,通过优化全局性能指标来提高优化性能.另外,该方法在优化问题中加入了一致性约束来限制关联子系统的估计状态与当前时刻更新的状态之间的偏差,进而保证各子系统优化问题初始可行时,后续时刻相继可行.在此基础上,通过加入终端约束来保证闭环系统渐进稳定.该方法能够在使用较少的通信和计算负荷情况下,提高系统优化性能.即使对于强耦合系统同样能够保证优化问题的递推可行性和闭环系统的渐进稳定性.仿真结果验证了本文所提出方法的有效性.
[Abstract]:In this paper, a non-iterative coordinated distributed predictive control method for distributed model predictive control systems, which can handle input constraints and ensure stability, is proposed for a class of distributed model predictive control systems.In this method, each controller communicates with other controllers only once to satisfy the limit of communication load, and improves the optimization performance by optimizing the global performance index.In addition, consistency constraints are added to the optimization problem to limit the deviation between the estimated state of the associated subsystem and the updated state at the current time, so as to ensure that when the optimization problem of each subsystem is initially feasible, the subsequent time will be feasible one after another.On this basis, terminal constraints are added to ensure the asymptotic stability of the closed-loop system.This method can improve the system optimization performance with less communication and load calculation.Even for strongly coupled systems, the recursive feasibility of the optimization problem and the asymptotic stability of the closed-loop system can be guaranteed.Simulation results show the effectiveness of the proposed method.
【作者单位】：上海交通大学自动化系系统控制与信息处理教育部重点实验室;
【基金】：Supported by National Natural Science Foundation of China(61233004,61590924,61673723)
【分类号】：TP273

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