To address the issues of large overshoot, long system stabilization time, and slow response associated with traditional PID-based and centralized model predictive control (MPC) of fan coil systems, this study proposes a distributed model predictive control (DMPC) method suitable for multi-zone building temperature regulation. Specifically, based on the thermal dynamic model of different zones, and aiming to constrain indoor temperature within set limits and minimize water system pressure drop and fan coil speed, a distributed MPC architecture for fan coil systems is developed. To solve the distributed optimization problem, the Nash equilibrium is introduced to find the global balance point within the distributed structure, and the Beetle Antennae Search-Particle Swarm Optimization (BAS-PSO) hybrid algorithm is used for online optimization of local MPCs. Experimental results show that compared to traditional PID control, the distributed MPC achieves coordinated multi-zone indoor temperature regulation with better dynamic performance, with the average absolute temperature error in each zone below 5 % and an energy savings rate of 16.23 %. Through semi-physical simulation, it is demonstrated that the proposed distributed MPC method has better applicability when the number of fan coils exceeds 10, compared to centralized MPC.

中文翻译：

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风机盘管系统分布式模型预测控制


针对传统风机盘管系统基于PID和集中式模型预测控制（MPC）的超调量大、系统稳定时间长、响应慢等问题，提出一种适用于多系统的分布式模型预测控制（DMPC）方法。 -区域建筑温度调节。具体而言，基于不同区域的热动态模型，以将室内温度限制在设定范围内、最小化水系统压降和风机盘管速度为目标，开发了风机盘管系统的分布式MPC架构。为了解决分布式优化问题，引入纳什均衡来寻找分布式结构内的全局平衡点，并使用甲虫天线搜索-粒子群优化（BAS-PSO）混合算法对局部MPC进行在线优化。实验结果表明，与传统PID控制相比，分布式MPC实现了多区域室内温度协调调节，动态性能更好，各区域平均绝对温度误差低于5%，节能率达16.23%。通过半实物仿真表明，与集中式MPC相比，当风机盘管数量超过10个时，所提出的分布式MPC方法具有更好的适用性。