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Plant-Wide Optimal Control with Decentralized MPC

机译:与分散MPC的植物广泛的最佳控制

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Most standard MFC implementations partition the plant into several units and apply MFC individually to these units. It is known that this strategy can lead to sub-optimal plant-wide control performance, especially if the units interact strongly. This paper tackles the problem of achieving optimal control performance in plants with such an MPC structure. A modeling framework, geared for use in MPC, that incorporates the interactions between the subsystems is employed. One may think that modeling the interactions and communicating the control actions between the controllers is sufficient to improve controller performance. We show that this idea is incorrect and can lead to closed-loop instability. A cooperation based MPC algorithm that converges to the plant-wide optimum is developed. In practical implementations, the cooperation based MPC scheme may have to be terminated before convergence is reached. To permit such flexibility, we propose a feasible cooperation based MPC algorithm. All cooperative iterates in this algorithm are feasible and the resulting MPC controller is closed-loop stable. Two examples comparing the performance of optimal and sub-optimal MPC controllers are presented.
机译:大多数标准MFC实现将工厂分配为几个单位并单独将MFC应用于这些单位。众所周知,该策略可以导致次优的植物广泛的控制性能,特别是如果单位强烈相互作用。本文解决了具有这种MPC结构的植物中实现最佳控制性能的问题。采用用于MPC的建模框架,其中包括子系统之间的相互作用。有人可以认为建模交互并传达控制器之间的控制操作足以提高控制器性能。我们表明这个想法不正确,可以导致闭环不稳定。开发了一种基于MPC算法,其融合到植物广泛的最佳。在实际实现中,基于合作的MPC方案可能必须在达到收敛之前终止。为了允许这种灵活性,我们提出了一种可行的基于MPC算法的合作。本算法中的所有合作迭代都是可行的,所产生的MPC控制器是闭环稳定的。提出了两个比较最佳和子最优MPC控制器性能的示例。

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