摘要
针对SCR脱硝系统大惯性、大迟延、多扰动问题,提出一种模型辅助迟延线性自抗扰控制(MDLADRC)。首先,利用模型信息,将高阶纯迟延系统补偿为一阶惯性环节;然后,根据闭环系统二自由度表达式,推导最优参数量化公式,简化参数调节,并通过参数遍历实验验证控制器参数与响应指标的关系;最后,将MDLADRC应用于选择性催化还原(SCR)脱硝控制系统,并与多种控制器性能进行比较。结果表明:对于SCR脱硝系统这类高阶大惯性大迟延系统,MDLADRC的定值跟随以及抗干扰能力更优;蒙特卡洛实验和多工况实验进一步说明MDLADRC的鲁棒性较优;此外,MDLADRC参数调节简单,更适用于工程实际。
To solve the problems of large inertia,large delay and multiple disturbances in SCR denitration system,a model-aided delay linear active disturbance rejection control(MDLADRC)was proposed.Firstly,the higher-order pure-delay system was compensated as a first-order inertial link using the model information.Secondly,according to the two-degree-of-freedom expression of the closed-loop system,the optimal parameter quantification formula was deduced.The parameter adjustment was simplified,and the relationship between the controller parameters and the response index was verified by parameter traversal experiments.Finally,MDLADRC was applied to the SCR denitration control system to compare the performance with various controllers.Results show that for high-order large-inertia and large-delay systems such as SCR denitration,MDLADRC has better fixed value following and anti-interference ability.Monte Carlo experiments and multi-condition experiments further illustrate the robustness of MDLADRC.In addition,the parameter adjustment of MDLADRC is simple,and is more suitable for engineering practice.
作者
杨超杰
刘长良
王梓齐
韩超
YANG Chaojie;LIU Changliang;VWANG Ziqi;HAN Chao(School of Control and Computer Engineering,North China Electric Power University,Beijing 100096,China;State Key Laboratory of New Energy Power System,North China Electric Power University,Baoding 071003,Hebei Province,China;Shijiazhuang Campus Department One,Army Engineering University of PLA,Shijiazhuang 050003,Hebei Province,China)
出处
《动力工程学报》
CAS
CSCD
北大核心
2023年第7期893-900,共8页
Journal of Chinese Society of Power Engineering
基金
北京市自然科学基金资助项目(4182061)
中央高校基本科研业务费专项资金资助项目(2018ZD05)。
关键词
SCR脱硝
自抗扰控制
史密斯预估
二自由度
蒙特卡洛实验
SCR denitrification
active disturbance rejection control
Smith prediction
two degrees of freedom
Monte Carlo experiment