基于改进粒子群优化IPSO算法的茶叶烘干机温度控制策略

针对茶叶烘干机热效率低,温度不稳定、茶叶品质不易保证的问题,对燃煤式热风干炉进行研究,通过对粒子群算法(PSO)进行混沌处理,得出了一种改进粒子群算法(IPSO),然后通过IPSO对模糊PID控制器的参数进行优化,以解决粒子群算法易早熟、寻优效率低以及PID参数无法实时在线调整的缺点。根据热风炉的实时温度,自动调节热风炉的排烟量,以实现烘干机的温度恒定。同时,采用优化后的模糊PID控制策略对系统进行了仿真和测试。结果表明,该研究所用方法能根据烘干机温度有效控制热风炉排烟量,从而实时控制热风温度,实现恒温控制的目的。

A strategy of temperature control in tea dryer based on improved particle swarmoptimization(IPSO)

Aiming at the problems of low thermal efficiency, unstable temperature and difficult guarantee of tea quality of tea dryer, the coal-fired hot air-drying furnace was studied in order to find an effective constant temperature control method. An improved particle swarm optimization (IPSO) algorithm was proposed by chaotic processing of particle swarm optimization (PSO). Then the parameters of fuzzy PID controller were optimized by IPSO to overcome the shortcomings of PSO, including premature, low optimization efficiency and real-time on-line adjustment of PID parameters. According to the real-time temperature of the hot blast stove, the amount of smoke discharged from the hot blast stove could be adjusted automatically to realize the constant temperature of the dryer. Moreover, the optimized fuzzy PID control strategy was used to simulate and test the system. The results showed that the method used in this study could effectively control the amount of smoke exhausted from the hot-blast stove according to the temperature of the dryer, so as to control the temperature of the hot-blast in real time and achieve the goal of constant temperature control. This method solved the traditional issues in controlling the temperature and reducing the energy consumption, and improved the thermal efficiency of the dryer and resulted in the guarantee of the good tea quality.