APPLYING NEURAL NETWORKS TO PID CONTROLLERS FOR TIME-DELAY SYSTEMS
Generalized PID neural network (GPIDNN) has recently received more attention in industry application. To investigate the control of long time-delay systems with GPIDNN control system, both the structure and the algorithm were presented in this paper, the real-time simulation to a main steam temperature control system was also carried out. The results show that GPIDNN is less sensitive to variation in the time-delay in comparison of conventional PID control system,it has short transition time and little over-adjust as well as ideal control quality. The results obtained during the present study indicate that GPIDNN has favorable control ability with self learning and self adapting; it is suitable substitute for conventional PID controllers for long time-delay systems.
PID controller neural networks time-delay system simulation
ZHUN YU YING-BAI XIE YOU-YIN JING XU-AO LU
Department of Power Engineering, North China Electric Power University, Baoding 071003, China
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
3173-3176
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）