改进粒子群优化的燃气发电锅炉主汽压模糊预测控制策略

Improved Particle Swarm Optimization Based Fuzzy Predictive Control Strategy for Main Steam Pressure in Gas-Fired Power Boiler

针对燃气发电锅炉存在的纯滞后、大惯性和参数模型易变等问题,设计了一种改进粒子群优化(PSO)的主汽压模糊广义预测控制策略。利用遗忘因子递推最小二乘法(FFRLS)辨识出主汽压模型,并引入广义预测控制(GPC),通过多步预测、滚动优化和实时反馈技术克服系统惯性、时滞和参数时变问题。为改善主汽压控制系统的稳定性和动态响应品质,对GPC算法中的控制加权系数进行模糊自校正设计。引入改进粒子群算法对广义预测控制的控制量增量进行寻优,求取最优控制律。仿真结果表明:与改进PSO-GPC策略和动态矩阵控制(DMC)策略相比,在施加扰动情况下,所提改进PSO-模糊GPC策略在模型适配与失配时稳定时间分别最多减少94.5 s和132 s,超调量分别最多降低5.1%和8%。工程运用表明:所提控制策略主汽压控制偏差低于±0.15 MPa,系统受模型失配影响更小,稳定性和抗扰动能力明显提升。

Aiming at the problems of pure lag,large inertia and variable parameter models in gas-fired power boilers,an improved particle swarm optimization(PSO)fuzzy generalized predictive control strategy for main steam pressure is designed.The main steam pressure model is identified by the forgetting factor recursive least squares method(FFRLS),and a generalized predictive control(GPC)is introduced to overcome system inertia,time delay and parameter time-varying via multi-step prediction,rolling optimization and real-time feedback technology.To improve the stability and dynamic response quality of the main steam pressure control system,the fuzzy self-tuning design of the control weighting coefficient in the GPC algorithm is carried out.Then an improved particle swarm algorithm is introduced to optimize the control variable increment of the generalized predictive control and obtain the optimal control law.Compared with the improved PSO-GPC strategy and the dynamic matrix control(DMC)strategy,the improved PSO-fuzzy GPC strategy reduces the stability time of model adaptation and mismatch by up to 94.5 s and 132 s respectively under disturbed condition.The overshoot is decreased by up to 5.1%and 8%respectively.Engineering application shows that the main steam pressure control deviation of the control scheme is lower than±0.15 MPa,the system is less affected by model mismatch,and the stability and anti-disturbance ability are significantly promoted.