针对电站锅炉汽电双驱给水系统在多源耦合与参数时变下的精准控制问题，提出了一种改进粒子群算法（IPSO）优化的模糊PID控制方法。IPSO通过非线性递减惯性权重、自适应学习因子及高斯变异三重机制提升全局寻优能力，并在线整定模糊PID的量化因子和增益修正量。仿真结果表明：与传统PID和模糊PID相比，在施加基准耦合刚度扰动情况下，IPSO-Fuzzy PID控制方法在调节时间分别减少102.4s和27.98s，超调量分别降低20.8%和7.2%。现场试验表明：在满负荷到低负荷下，该策略将出口压力最大稳态偏差控制在±0.5MPa，显著提升了系统的动态响应、稳态精度与鲁棒性。

To address the precise control challenges of steam-electric dual-drive water supply system under multi-source coupling and time-varying parameters, a fuzzy PID control method optimized with an improved particle swarm optimization algorithm (IPSO) was proposed. IPSO utilizes a triple mechanism of nonlinear decreasing inertia weight, adaptive learning factor, and Gaussian variation to enhance global optimization capabilities and online tune the quantization factor and gain correction of the fuzzy PID. Simulation results show that, compared with traditional PID and fuzzy PID control, the IPSO-Fuzzy PID control method reduces settling time by 102.4 seconds and 27.98 seconds, respectively, and overshoot by 20.8% and 7.2%, respectively, under a baseline coupling stiffness perturbation. Field tests demonstrate that this strategy maintains the maximum steady-state deviation of the outlet pressure within ±0.5 MPa from full to low load, significantly improving the system's dynamic response, steady-state accuracy, and robustness.

上海市市场监督管理局科技项目(2024-44)