在循环流化床锅炉(Circulating Fluidized Bed Boiler,CFBB)燃烧控制系统中,维持正常的床温和主蒸汽压力是循环流化床锅炉稳定、经济运行的关键.笔者针对这两个变量的强耦合特性,引入神经网络逆系统方法,实现系统的线性化解耦,并针对线性化解耦的非理想性以及参数、干扰等不确定因素对系统的影响,采用具有较好稳定性和抗干扰能力的内模控制策略对系统进行闭环控制.仿真结果表明,基于神经网络逆系统的内模控制方法不仅能够实现系统解耦,获得优良的静、动态特性,且具有良好的鲁棒稳定性和抑制扰动的能力.
In the standard particle swarm optimization(SPSO),the big problem is that it suffers from premature convergence,that is,in complex optimization problems,it may easily get trapped in local optima.In order to mitigate premature convergence problem,this paper presents a new algorithm,which is called particle swarm optimization(PSO) with directed mutation,or DMPSO.The main idea of this algorithm is to "let the best particle(the smallest fitness of the particle swarm) become more excellent and the worst particle(the largest fitness of the particle swarm) try to be excellent".The new algorithm is tested on a set of eight benchmark functions,and compared with those of other four PSO variants.The experimental results illustrate the effectiveness and efficiency of the DMPSO.The comparisons show that DMPSO significantly improves the performance of PSO and searching accuracy.
