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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.
In this paper, we propose a distributed model predictive control (DMPC) that can handle the guaranteed stability of input constraints for a distributed system consisting of state-coupled subsystems. , Each controller solves the control rate only communicate with other controllers only once to meet the communication load limit of the system, and at the same time, it improves the optimization performance by optimizing the global performance index.In addition, this method adds the consistency constraint in the optimization problem So as to limit the deviation between the estimated state of the associated subsystem and the state updated at the current time so as to ensure that the subsequent optimization of the subsystems becomes feasible when the initial optimization is feasible.On the basis of this, the closed-loop system is guaranteed to be asymptotically stable by adding terminal constraints This method can improve system optimization performance with less communication and computational load, even for strongly coupled systems, the recursive optimization of the optimization problem and the asymptotic stability of the closed-loop system are also guaranteed. The simulation results verify that this paper Put forward the effectiveness of the method.