当前供热控制系统使用传统PID控制,造成供热效果差、能源浪费现象严重。通过海鸥优化算法优化PID参数,同时针对海鸥优化算法收敛精度差、速度慢等问题,提出一种改进海鸥优化算法（IGSOA）。在海鸥迁徙期引入余弦函数收敛因子以维持探索能力并加速后期收敛,在海鸥捕食期则结合贪婪策略与加权平均更新机制,引导种群向优势区域迁移。最后加入黄金正弦策略指引种群位置更新,提高局部搜索能力。本文建立供热控制系统的数学模型,利用所得实验数据计算出系统的传递函数,然后利用Matlab中的Simulink搭建并运行IGSOA-PID控制器。实验结果表明,改进后的PID控制器响应快、超调量低、稳定性好,提高了供热控制系统的控制效果。
Abstract
Current heating control systems relying on traditional PID control exhibit suboptimal performance, leading to poor heating effectiveness and significant energy wastage. This paper addresses these limitations by employing the Seagull Optimization Algorithm (SOA) to optimize PID controller parameters. To mitigate the inherent shortcomings of SOA, such as limited convergence accuracy and slow convergence speed, an Improved Seagull Optimization Algorithm (IGSOA) is proposed. The IGSOA incorporates a cosine function convergence factor during the seagull migration phase to sustain exploratory capabilities while accelerating convergence in later stages. In the predatory phase, a combination of a greedy strategy and a weighted-average updating mechanism is introduced to guide the population toward promising regions of the search space. Furthermore, a golden-section-based sine strategy is integrated to guide population position updates, thereby enhancing local search capabilities. To evaluate the proposed approach, a mathematical model of a heating control system is established. Experimental data obtained from the system is used to derive its transfer function. The IGSOA-PID controller is then implemented and simulated using Simulink in MATLAB. Experimental results demonstrate that the proposed controller exhibits a faster response, lower overshoot, and improved stability, ultimately enhancing the control effectiveness of the heating control system.
关键词
供热控制系统 /
PID控制 /
改进海鸥优化算法 /
Matlab
Key words
heating control system /
PID control /
improved seagull optimization algorithm /
Matlab
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 孟亚男,黄迎旭.基于粒子群优化的神经网络PID控制器在供热系统的研究[J].吉林化工学院学报,2023,40(11):50-53.
[2] 李晓慧. 城市集中供热控制系统研究[J].今日制造与升级,2024(9):17-20.
[3] 许乐,莫愿斌,卢彦越.基于改进海鸥优化算法的PID控制器参数优化[J].机床与液压,2021,49(16):17-23.
[4] 时宇辉,袁至,王维庆,等.基于改进海鸥算法优化SVM的变压器故障诊断方法[J].科学技术与工程,2024,24(28):12169-12176.
[5] EWEES AHMED A,MOSTAFA REHAM R.,GHONIEM RANIA M.,et al.Improved seagull optimization algorithm using Lévy flight and mutation operator for feature selection[J].Neural computing & applications,2022,34(10):7437-7472.
[6] 严爱军,胡开成.提高海鸥优化算法寻优能力的改进策略及其应用[J].信息与控制,2022,51(6):688-692,694-698.
[7] 王荣鑫,葛振福,侯晨晨.Smith预估补偿与积分分离PID在供热控制系统中的应用[J].工业仪表与自动化装置,2024(4):71-74.
[8] 兰庆楼.一种智能混水供热机组控制系统:CN202421068857.2[P].2024-12-24.
[9] 张锋. 基于模糊PID的区域供热锅炉主汽温控制方法[J].工业加热,2023,52(12):19-23.
[10] YUWEI LIU,LINGLING LI,JIAQI LIU.Short-term wind power output prediction using hybrid-enhanced seagull optimization algorithm and support vector machine: A high-precision method[J].International journal of green energy,2024,21(9/12):2858-2871.
[11] 李大海,熊文清,王振东.融合多策略的增强海鸥优化算法[J].计算机应用研究,2023,40(3):717-724.
[12] 中国计量大学.一种多策略改进的海鸥优化算法:CN202311398926.6[P].2024-12-20.
[13] 吴伟开,何雪明,黄育鹏.基于海鸥算法的库区货位分配及优化研究[J].轻工机械,2023,41(4):91-98.
[14] 黄训爱,杨光永,蔡艳,等.基于改进海鸥优化算法的机器人路径规划[J].组合机床与自动化加工技术,2024(5):21-25,32.
[15] 王翔. 工业水泵自动化变频改造经济性分析[J].现代工业经济和信息化,2023,13(10):127-128,132.
[16] WANG,RU,TAN,CHAO,XU,JING,et al.Pressure Control for a Hydraulic Cylinder Based on a Self-Tuning PID Controller Optimized by a Hybrid Optimization Algorithm[J].Algorithms,2017,10(1):19.
[17] 胡涛,蒋全.基于改进蜜獾优化算法的PID参数整定[J].电子科技,2023,36(12):46-54.
