【摘要】 针对SCR脱硝控制系统中的PID参数整定困难,提出一种优化粒子群算法的PID控制器参数整定方法。该算法利用已知粒子信息得到适应度函数值的估值,在估值策略中,引入粒子间的相似度和粒子的可信度评价方法,增加适应度函数值估计次数和准确性,减少适应度函数值计算次数,提高算法性能。将优化的粒子群算法用于优化SCR脱硝系统中PID控制器参数,与传统整定PID控制器参数方法相比,基于适应值估值策略的粒子群算法整定PID控制器参数收敛速度快,系统内回路响应上升速度快、时间短,外回路静态误差小、无超调等优点,较好地满足SCR脱硝系统的动态特性。更多还原

【Abstract】 Aiming at the difficulty of PID parameter setting in SCR denitration control system, a PID controller parameter tuning method based on particle swarm optimization algorithm is proposed. The algorithm uses the known particle information to obtain the approximation of the fitness function value. In the evaluation strategy, the similarity between particles and the credibility evaluation method of the particle are introduced to increase the probability and accuracy of the fitness function value estimation and reduce the adaptation. The degree of function value is calculated to improve the performance of the algorithm. The optimized particle swarm optimization algorithm is used to optimize the PID controller parameters in the SCR denitration system. Compared with the traditional tuning PID controller parameter method, the particle swarm optimization algorithm based on the adaptive value estimation strategy converges the PID controller parameters quickly. The loop response rises quickly, and the time is short; the external loop static error is small, and there is no overshoot, which better satisfies the dynamic characteristics of the SCR denitration system.更多还原