為解決單機架爐卷軋機監(jiān)控AGC 的大時滯和控制精度問題，針對常規(guī)PID 不能自我進(jìn)化的缺點，本文采用BP 神經(jīng)網(wǎng)絡(luò)對PID 控制器進(jìn)行參數(shù)自整定，并將該智能控制器與Smith 預(yù)估器結(jié)合對AGC 系統(tǒng)進(jìn)行監(jiān)控，既解決了自我進(jìn)化問題，也克服了時滯，通過仿真分析和現(xiàn)場驗證，其收斂速度和魯棒性明顯改善。
關(guān)鍵詞：爐卷軋機；監(jiān)控AGC；智能PID；Smith 預(yù)估器
Abstract: Considered the general PID can’t improve itself, a BP neural network PID is proposed to improve the control precision and large time deday of monitor AGC of a Steckel rolling mill. And then the intelligent PID and Smith prediction control machine are combined to minitor the AGC system so that the self-learning capability and time delay of the system evidently improved. It was proved that the robustness and convergence rate of the system
is obviously improved by simulation and implement experiment.
Key words: Steckel mill; monitor AGC; intelligent PID; smith prediction control machine