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作 者:李艳[1,2] 王鑫 胡振[1] 戴庆瑜 LI Yan;WANG Xin;HU Zhen;DAI Qing-yu(School of Electrical and Control Engineering,Shannxi University of Science&Technology,Xi′an 710021,China;Shaanxi Agricultural Products Processing Technology Research Institute,Xi′an 710021,China)
机构地区:[1]陕西科技大学电气与控制工程学院,陕西西安710021 [2]陕西省农产品加工技术研究院,陕西西安710021
出 处:《陕西科技大学学报》2023年第4期143-150,共8页Journal of Shaanxi University of Science & Technology
基 金:陕西省科技厅重点研发计划项目(2023-YBGY-277);陕西省咸阳市重点研发计划项目(L2022ZDYFSF047)。
摘 要:有机废液焚烧过程存在强耦合、强干扰、非线性、不确定性等问题导致以数学模型为基础的控制策略较难取得良好的控制效果.LADRC可利用LESO对以上因素进行估计并补偿,但当观测带宽受限时会导致LESO观测能力不足,从而使得控制效果不佳.针对以上问题,本文对LADRC进行改进,首先将实际系统转化为积分串联形式的理想系统,然后分别将控制信号作用于理想系统和实际系统并得出二者的输出偏差,再采用PI控制器对其进行补偿,弥补了因低增益LESO观测能力不足导致控制效果欠佳的缺陷.将改进后的LADRC应用于有机废液焚烧系统,仿真结果表明改进后的LADRC采用低增益LESO不仅能有效解决以上所描述的有机废液焚烧过程中存在的各控制难点,并且相较传统的LADRC具有更好的阶跃跟踪能力、抗干扰性和鲁棒性.The organic waste liquid incineration process has problems such as strong coupling,strong interference,non-linearity,uncertainty,etc.,which makes it difficult to achieve good control effects for control strategies based on mathematical models.LADRC can use LESO to estimate and compensate for the above factors,but when the observation bandwidth is limited,it will lead to insufficient LESO observation ability,which makes the control effect poor.This article improves LADRC.First,the actual system is transformed into an ideal system in integral series,and then the control signal is applied to the ideal system and the actual system to obtain the output deviation of the two,and then the PI controller is used to compensate it,it makes up for the defect of poor control effect due to insufficient observation ability of low-gain LESO.Applying the improved LADRC to the organic waste incineration system,the simulation results show that the improved LADRC using low-gain LESO can not only effectively solve the control difficulties in the organic waste incineration process described above,but also has better step tracking ability,anti-jamming and robustness than the traditional LADRC.
关 键 词:废液焚烧 LADRC控制 低增益LESO PI补偿 蒙特卡洛实验
分 类 号:TP273[自动化与计算机技术—检测技术与自动化装置]
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