摘要
为了提高对复杂工况下的轧机系统控制要求,对二辊液压轧机辊缝控制进行研究。为轧机系统控制过程构建了开环传递函数,同时运用蚁群算法(ACO)与人工鱼群算法(AFSO)优化了PID控制器的各项参数,最后利用Simulink对比了优化处理后的系统响应速率与抗干扰性。阶跃信号未施加干扰力下,AFSO获得了比ACO更低的超调量,降低幅度达到13.61%,同时缩短了21.00%的调整时间,并且稳态误差也减小近30.00%,表明采用AFSO可以达到比ACO更优的响应性能。阶跃信号施加干扰力下,采用AFSO优化的系统,响应曲线超调量下降了12.58%,调整时间缩短了14.58%,稳态误差降低了25.00%。逐渐提高随机信号频率后,AFSO都比ACO表现出了更低的随机信号响应曲线波动范围,表明AFSO具备比ACO更优的响应控制效果。
In order to improve the control requirements of the rolling mill system under complex working conditions,the roll gap of two high hydraulic mill control was studied.An open loop transfer function was constructed for the control process of the system.At the same time,ant clony optimization(ACO)and artificial fish swarm optimization(AFSO)were used to optimize the parameters of PID controller.Finally,the response rate and anti-interference of the optimized system were compared with Simulink.The results show that the step signal without interference force,AFSO achieves a lower overshoot than ACO,with a reduction of 13.61%and a reduction of 21.00%adjustment time,and the steady-state error is also reduced by nearly 30.00%,indicating that AFSO can achieve better response performance than ACO.Under the step signal applied the interference force,the response curve overshooting amount decreased by 12.58%,the adjustment time decreased by 14.58%,and the steady-state error decreased by 25.00%by the AFSO optimized system.After the random signal frequency was gradually increased,AFSO showed lower fluctuation range of random signal response curve than ACO,indicating that AFSO had better response control effect than ACO.
作者
罗彩玉
刘明
LUO Caiyu;LIU Ming(School of Mechanical and Electrical Engineering,Aksu Vocational and Technical College,Wensu 843100,Xinjiang,China;School of Electrical Engineering,North China University of Science and Technology,Tangshan 063210,Hebei,China)
出处
《中国工程机械学报》
北大核心
2022年第4期310-314,共5页
Chinese Journal of Construction Machinery
基金
河北省自然科学基金资助项目(B2016209059)。
