基于扩张观测器的间歇过程迭代学习自抗扰控制OA
Iterative learning active disturbance rejection control based on extended state observer for batch process
针对一类包含不确定性和非重复性的间歇过程鲁棒性问题,设计了一种基于扩张状态观测器(ESO)的比例-微分(PD)型迭代学习控制(ILC)策略.通过 ESO 估计系统受到的总扰动,结合 PD 型间接迭代学习控制律对自抗扰控制器进行批次轴上的迭代更新,实现批次轴和时间轴两个方向上的控制律修正优化.此外,对间歇反应釜和橡胶密炼过程的仿真结果表明,所提出的基于扩张观测器的自抗扰迭代学习策略能有效抑制外部不确定干扰,改善系统的鲁棒性与控制精度.
To address the robustness issues in a class of batch processes characterized by uncertainty and non-repeatability,a proportional-derivative(PD)type iterative learning control(ILC)strategy based on an extended state observer(ESO)was designed.The ESO was employed to estimate the total disturbances acting on the system,and an indirect PD-type ILC law was applied to iteratively update the disturbance-rejection controller along the batch axis.This enabled joint optimization of the control law in both the time and batch domains.Simulation results on a batch reactor and a rubber internal mixing process demonstrated that the proposed ESO-based iterative disturbance-rejection learning strategy effectively suppressed external uncertain disturbances and improved the system's robustness and control accuracy.
潘颖;薄翠梅;何文敏;李俊
南京工业大学 电气工程与控制科学学院,江苏 南京 211816南京工业大学 电气工程与控制科学学院,江苏 南京 211816南京工业大学 智能制造研究院,江苏 南京 210009南京工业大学 电气工程与控制科学学院,江苏 南京 211816
信息技术与安全科学
间歇过程迭代学习控制PD型迭代学习自抗扰控制扩张状态观测器
batch processesiterative learning controlPD-type iterative learningactive disturbance reject controlextended state observer
《高校化学工程学报》 2026 (2)
262-270,9
国家重点研发计划(2022YFB3305300)国家自然科学基金(62333010,62173178).
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