通讯信息约束下具有全局稳定性的分布式系统预测控制

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.

Global stabilizing distributed model predictive control systems with limited communication

A novel stabilized distributed model predictive control (DMPC) with input constraints and global costoptimization coordination strategy is proposed for spatially distributed coupling systems which are presented by statesinteracted models. The distributed controllers make decisions locally and merely communicate once a control period witheach others. Cooperation is promoted by consideration of the system-wide objective by each local controller. Consistencyconstraints, which bound the estimation errors of the interaction sequences among subsystems, are designed to guaranteethat, if an initially feasible solution can be found, subsequent feasibility of the algorithm is guaranteed at every update, andthat the closed-loop system is asymptotically stable. The proposed control algorithm could reduce the communication andcomputation loads with improved performance of entire systems, and guarantee the recursive feasibility and the asymptoticallystability even when the controlled subsystems are strong coupled. Simulation results show that the performance ofthe proposed DMPC is very close to that of a centralized model predictive control (MPC).