Coordinating distributed MPC efficiently on a plantwide scale: The Lyapunov envelope algorithm
Coordinating distributed MPC efficiently on a plantwide scale: The Lyapunov envelope algorithm
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DOI：
10.1016/j.compchemeng.2021.107532
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发表时间：
2021-09
期刊：
影响因子：
--
通讯作者：
Wentao Tang;P. Daoutidis
中科院分区：
文献类型：
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作者：
Wentao Tang;P. Daoutidis
大规模系统的模型预测控制（MPC）应采用分布式优化方法，即由各子系统的控制器优化其控制行为，并通过迭代协调各子系统的决策。然而，MPC的实时实现通常允许非常有限的计算时间，并且不可避免地需要提前终止。在这项工作中，我们提出了一个分裂算法的分布式优化类似于前向-后向分裂（FBS），其中的101和二次罚款施加在违反子系统之间的互连关系。通过设计所涉及的参数的基础上耗散分析，迭代导致在一个工厂范围的李雅普诺夫函数，我们称之为李雅普诺夫包络的单调下降，从而保持闭环稳定性下的分布式MPC，尽管提前终止，并产生改善控制性能的允许的计算时间或迭代次数的增加。在工业规模的醋酸乙烯酯单体工艺上测试了所提出的李雅普诺夫包络算法。
The model predictive control (MPC) of large-scale systems should adopt a distributed optimization approach, where controllers for the constituent subsystems optimize their control actions and iterations are used to coordinate their decisions. The real-time implementation of MPC, however, usually allows very limited time for computation and inevitably needs to be terminated early. In this work, we propose a splitting algorithm for distributed optimization analogous to forward-backward splitting (FBS), where ℓ 1 and quadratic penalties are imposed on the violation of interconnecting relations among the subsystems. By designing the involved parameters based on dissipative analysis, the iterations result in the monotonic decrease of a plant-wide Lyapunov function, which we call Lyapunov envelope, thus maintaining closed-loop stability under distributed MPC despite early termination and yielding improving control performance as the allowed computational time or number of iterations increases. The proposed Lyapunov envelope algorithm is tested on an industrial-scale vinyl acetate monomer process.