常规的轧钢加热炉煤气智能燃烧控制方法主要使用Fuzzy双交叉限幅控制器进行控制阶跃响应,易受温变超调作用的影响,导致燃烧效率偏低。基于此,提出一种基于比例-积分-微分(Proportion Integral Differential,PID)算法的轧钢加热炉煤气智...常规的轧钢加热炉煤气智能燃烧控制方法主要使用Fuzzy双交叉限幅控制器进行控制阶跃响应,易受温变超调作用的影响,导致燃烧效率偏低。基于此,提出一种基于比例-积分-微分(Proportion Integral Differential,PID)算法的轧钢加热炉煤气智能燃烧控制方法。生成轧钢加热炉煤气智能燃烧控制策略,利用PID算法设计轧钢加热炉煤气智能燃烧控制器,从而实现轧钢加热炉煤气智能燃烧控制。实验结果表明,设计的轧钢加热炉煤气智能燃烧PID算法控制方法在不同控制起始时间下的煤气智能燃烧效率均较高,控制性能良好,具有较高的实际应用价值。展开更多
Due to the difficulty of controlling the process with inverse response and dead time,a Multi-objective Optimization based on Genetic Algorithm (MOGA) method for tuning of proportional-integral-derivative (PID) control...Due to the difficulty of controlling the process with inverse response and dead time,a Multi-objective Optimization based on Genetic Algorithm (MOGA) method for tuning of proportional-integral-derivative (PID) controller is proposed. The settings of the controller are valued by two criteria,the error between output and reference signals and control moves. An appropriate set of Pareto optimal setting of the PID controller is founded by analyzing the results of Pareto optimal surfaces for balancing the two criteria. A high order process with inverse response and dead time is used to illustrate the results of the proposed method. And the efficiency and robustness of the tuning method are evident compared with methods in recent literature.展开更多
为了解决精密加工设备的微位移隔振问题,研制了一种以压电陶瓷为作动器的智能微位移主动隔振系统。在现有数据采集系统和激振器的基础上搭建了相应的实验平台,提出将模糊-比例积分微分(fuzzy-proportional integral derivative,简称Fuzz...为了解决精密加工设备的微位移隔振问题,研制了一种以压电陶瓷为作动器的智能微位移主动隔振系统。在现有数据采集系统和激振器的基础上搭建了相应的实验平台,提出将模糊-比例积分微分(fuzzy-proportional integral derivative,简称Fuzzy-PID)算法理论应用到微位移的主动隔振控制中,在实验室虚拟仪器工程平台(laboratory virtual instrumentation engineering workbench,简称LabVIEW)环境下开发了整个系统的算法控制程序,分别在扫频、随机和正弦激励信号下进行了微位移主动隔振实验。实验结果表明,受控后的振动位移大幅度降低,验证了该方法对微位移主动隔振的有效性。展开更多
文摘常规的轧钢加热炉煤气智能燃烧控制方法主要使用Fuzzy双交叉限幅控制器进行控制阶跃响应,易受温变超调作用的影响,导致燃烧效率偏低。基于此,提出一种基于比例-积分-微分(Proportion Integral Differential,PID)算法的轧钢加热炉煤气智能燃烧控制方法。生成轧钢加热炉煤气智能燃烧控制策略,利用PID算法设计轧钢加热炉煤气智能燃烧控制器,从而实现轧钢加热炉煤气智能燃烧控制。实验结果表明,设计的轧钢加热炉煤气智能燃烧PID算法控制方法在不同控制起始时间下的煤气智能燃烧效率均较高,控制性能良好,具有较高的实际应用价值。
基金National Natural Science Foundation of China (No.60504033)
文摘Due to the difficulty of controlling the process with inverse response and dead time,a Multi-objective Optimization based on Genetic Algorithm (MOGA) method for tuning of proportional-integral-derivative (PID) controller is proposed. The settings of the controller are valued by two criteria,the error between output and reference signals and control moves. An appropriate set of Pareto optimal setting of the PID controller is founded by analyzing the results of Pareto optimal surfaces for balancing the two criteria. A high order process with inverse response and dead time is used to illustrate the results of the proposed method. And the efficiency and robustness of the tuning method are evident compared with methods in recent literature.
