基于改进TLBO算法的刮板输送机伸缩机尾PID控制系统优化

Optimization of scraper conveyor extensible tail PID control system based on improved TLBO algorithm

为了提高刮板输送机伸缩机尾控制系统的工作性能,将一种新的群智能优化算法,即教学与学习算法(TLBO)应用于机尾PID控制器的参数优化中,并提出新的自适应教学因子计算方法,其利用完整学习阶段前、后学生群体成绩的变化来决定教学因子的取值。研究结果表明:改进后的TLBO算法的精度及稳定性均比原TLBO算法的优。在建立刮板输送机伸缩机尾控制系统模型的基础上,利用改进的TLBO方法进行PID参数整定,并引入超调量控制指标对适应度函数再次完善,二次优化后的刮板输送机伸缩机尾控制系统具有良好控制品质和鲁棒性。

In order to improve the work performance of scraper conveyor extensible tail control system, a new swarm intelligence optimization algorithm, i.e. teaching learning based optimization （TLBO） algorithm was used to optimize the tail＇s PID controller parameters. And a new adaptive calculation method of teaching factor was proposed, making use of the variation of the marks obtained by the learners before and after a complete learning phase to determine the values of teaching factors. The results show that the precision and stability of improved TLBO algorithm are superior to those of standard TLBO. On the basis of the established model of scraper conveyor extensible tail control system, the PID controller parameters are tuned by the improved TLBO algorithm. Furthermore the overshoot volume control index is introduced to the fitness function to improve the PID optimization. The secondary optimized control system of scraper conveyor extensible tail has good control quality and robustness.