Distributed model predictive control of dynamically decoupled systems with coupled cost
Distributed model predictive control of dynamically decoupled systems with coupled cost
复制标题
DOI：
10.1016/j.automatica.2010.09.002
复制
发表时间：
2010
期刊：
影响因子：
--
通讯作者：
Chen Wang;C. Ong
中科院分区：
文献类型：
--
作者：
Chen Wang;C. Ong
本文研究了具有相互作用的子系统的分布式模型预测控制（DMPC）问题，这些子系统具有解耦的动态和约束，但具有耦合的代价。一个易于验证的约束，以确保在没有干扰的情况下，整个系统的渐近稳定性。引入的约束具有允许DMPC系统的性能接近由集中式模型预测控制器控制的性能的参数。当子系统是线性的且存在加性扰动时，所增加的约束保证每个子系统的状态收敛到其各自的最小扰动不变集。通过几个数值例子证明了这种方法。
This paper considers the distributed model predictive control (DMPC) of systems with interacting subsystems having decoupled dynamics and constraints but coupled costs. An easily-verifiable constraint is introduced to ensure asymptotic stability of the overall system in the absence of disturbance. The constraint introduced has a parameter which allows for the performance of the DMPC system to approach that controlled by a centralized model predictive controller. When the subsystems are linear and additive disturbance is present, the added constraint ensures the state of each subsystem converges to its respective minimal disturbance invariant set. The approach is demonstrated via several numerical examples.