Abstract Shifting away from the traditional mass production approach of the process industry, towards more agile, cost-effective and dynamic process operation, provides motivation for next-generation smart plants. The control system for smart plants needs to be capable of dynamically handling a wide range of operating conditions, whilst minimising operation costs during transitions, in addition to efficiently dealing with large-scale systems. This article presents a flexible and scalable Distributed Model Predictive Control (DMPC) approach based on differential dissipativity, which permits arbitrary cost functions (including economic costs). First, a plantwide contraction condition that ensures convergence to any feasible setpoint is constructed based on the process network topology and control synthesis of individual subsystems. This condition is then imposed as a constraint on local MPC controllers in a distributed manner, resulting in a scalable DMPC scheme where individual subsystems minimise arbitrary cost functions, whilst sharing the responsibility for plantwide stability.

中文翻译：

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基于微分耗散的分布式 MPC 用于非线性全厂系统的灵活运行

摘要 从流程工业的传统大规模生产方法转向更灵活、更具成本效益和动态的流程操作，为下一代智能工厂提供动力。除了高效处理大型系统之外，智能工厂的控制系统还需要能够动态处理各种运行条件，同时最大限度地降低过渡期间的运行成本。本文提出了一种基于微分耗散的灵活且可扩展的分布式模型预测控制 (DMPC) 方法，该方法允许任意成本函数（包括经济成本）。首先，基于过程网络拓扑结构和各个子系统的控制综合，构建了确保收敛到任何可行设定点的全厂收缩条件。