Hierarchical Coordinated Predictive Control of Multiagent Systems for Process Industries

Abstract

Focusing on the requirements for efficient and accurate control in large-scale process industries with integrated “distributed-decentralized” characteristics, a novel hierarchical coordinated predictive control strategy for process industries is proposed with a multiagent system as the computational paradigm. This approach comprehensively considers the overall state of the system, the interactions of control actions among agents, the constraints of processes and energy consumption to solve the problems of poor flexibility of agent decision-making in the narrow consensus strategy and strong interaction of parts of the system. The proposed hierarchical control strategy requires each agent to perform three tasks at each time step. First, each agent iteratively obtains a consistent basis for closed-loop prediction in a distributed way. Then, each agent independently proposes a control scheme and determines its own priority by playing games based on the economic performance of the scheme. Next, each agent calculates its own optimal dynamic predictive control sequence in order of priority based on the system’s dynamic process model. Finally, by considering the temperature-control process of heating an alumina ceramic block in a high-power microwave reactor with six microwave sources, the effectiveness of the proposed hierarchical coordinated predictive control strategy is verified under different communication topologies by comparing it with the centralized model predictive control strategy.