论文部分内容阅读
纸机干燥部的蒸汽压力控制直接影响蒸汽消耗量以及纸张的质量,针对干燥部的工况多变,选用传统PID不易控制,标准粒子群算法寻优精度不高、易导致早熟现象等缺陷,提出一种改进的粒子群算法来自整定PID参数,通过引入线性递减的惯性权重ω来协调PSO的全局和局部寻优能力。MATLAB仿真实验表明,采用改进的粒子群算法整定后的干燥部压力控制PID有更好的鲁棒性和响应速度。
The steam pressure control of the drying section of the paper machine has a direct impact on the steam consumption and the quality of the paper. In view of the changing conditions of the dryer section, the traditional PID control is not easy to control, the accuracy of the standard particle swarm optimization is not high, An improved Particle Swarm Optimization (PSO) algorithm is proposed from tuning PID parameters. The linear and decreasing inertia weight ω is used to coordinate the global and local optimization ability of PSO. MATLAB simulation results show that the improved Particle Swarm Optimization (PSO) has better robustness and response speed to PID control of drying section.