工矿自动化2026，Vol.52Issue(7)：85-92,8.DOI:10.13272/j.issn.1671-251x.2026030116
基于改进BSO-模糊PID的矿用通风机风量智能调控
Intelligent air volume control of mine ventilators based on improved BSO-fuzzy PID
摘要
Abstract
For fuzzy PID control of mine ventilators,the core tuning parameters are determined empirically,cannot be updated autonomously,and are difficult to adapt to complex and variable mine ventilation conditions.To address these problems,an intelligent air volume control method for mine ventilators based on improved Beetle Swarm Optimization(BSO)-fuzzy PID control was proposed.To overcome the imbalance between global search and local convergence of BSO and its tendency to become trapped in local optima,the parameter update mechanism of BSO was adaptively improved in terms of inertia weight,learning factors,and search step size.The improved BSO was used to optimize five parameters comprising the quantization factors and scaling factors of fuzzy PID control.The optimal parameters were assigned to the fuzzy PID controller,and the fuzzy PID control parameters were corrected in real time according to the air volume feedback signal,thereby achieving intelligent air volume control of the mine ventilator.Simulation results showed that,at the set air volume,the rise time of the improved BSO-fuzzy PID controller was 16.2％shorter than that of the fuzzy PID controller,the overshoot was reduced by 92.70％,and the settling time was shortened by 62.45％.Under large time-delay and pulse-disturbance conditions,compared with the PID and fuzzy PID controllers,the improved BSO-fuzzy PID controller exhibited a smaller oscillation amplitude,faster steady-state recovery,and stronger robustness and disturbance rejection.Under air volume variation and parameter perturbation conditions,the improved BSO-fuzzy PID controller had the shortest rise time and settling time and the smallest overshoot.Experimental results showed that the air volume tracking response curve of the ventilator under improved BSO-fuzzy PID control exhibited no obvious overshoot or persistent severe oscillations,and that the air volume could track the set value and stabilize near it within a short time.关键词
通风机/风量调控/模糊PID控制/天牛群优化算法/参数寻优Key words
ventilator/air volume control/fuzzy PID control/Beetle Swarm Optimization/parameter optimization分类
矿业与冶金引用本文复制引用
李刚,刘勇,徐光,王凯,卢锋,王志静..基于改进BSO-模糊PID的矿用通风机风量智能调控[J].工矿自动化,2026,52(7):85-92,8.基金项目
国家自然科学基金资助项目(52374242). （52374242）
