An Improved Formulation of Hybrid Model Predictive Control With Application to Production-Inventory Systems.
An Improved Formulation of Hybrid Model Predictive Control With Application to Production-Inventory Systems.
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DOI：
10.1109/tcst.2011.2177525
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发表时间：
2013-01-01
期刊：
影响因子：
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通讯作者：
Rivera DE
中科院分区：
文献类型：
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作者：
Nandola NN;Rivera DE
我们考虑一种针对由混合逻辑动态（MLD）模型描述的线性混合系统的改进型模型预测控制（MPC）公式。该算法依赖于多自由度参数化，使用户能够在闭环系统中独立调整设定值跟踪速度、可测干扰抑制和不可测干扰抑制。因此，与传统MPC方案中依赖目标函数权重（例如移动抑制）相比，控制器的调整更加灵活和直观。控制器公式是由混合生产 - 库存系统所恰当描述的非传统控制应用的需求所推动的。本文考虑了两个应用：行为健康中的自适应、时变干预，以及有限产能条件下供应链中的库存管理。在自适应干预应用中，研究了一个受“快速通道”项目启发的假设干预，这是一个用于减少高危儿童行为障碍的现实生活中的预防性干预。在库存管理应用中，展示了该算法在需求变化的条件下明智地改变生产能力的能力。这些案例研究表明，针对混合系统的MPC可以在涉及噪声和不确定性的苛刻条件下针对期望性能进行调整。
We consider an improved model predictive control (MPC) formulation for linear hybrid systems described by mixed logical dynamical (MLD) models. The algorithm relies on a multiple-degree-of-freedom parametrization that enables the user to adjust the speed of setpoint tracking, measured disturbance rejection and unmeasured disturbance rejection independently in the closed-loop system. Consequently, controller tuning is more flexible and intuitive than relying on objective function weights (such as move suppression) traditionally used in MPC schemes. The controller formulation is motivated by the needs of non-traditional control applications that are suitably described by hybrid production-inventory systems. Two applications are considered in this paper: adaptive, time-varying interventions in behavioral health, and inventory management in supply chains under conditions of limited capacity. In the adaptive intervention application, a hypothetical intervention inspired by the Fast Track program, a real-life preventive intervention for reducing conduct disorder in at-risk children, is examined. In the inventory management application, the ability of the algorithm to judiciously alter production capacity under conditions of varying demand is presented. These case studies demonstrate that MPC for hybrid systems can be tuned for desired performance under demanding conditions involving noise and uncertainty.