基于改进型Smith预估器的同步施工网络控制系统
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TH69;TP273

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国家自然科学基金资助项目(51575398);国家科技支撑计划资助项目(2015BAF07B05);上海市自然科学基金资助项目(15ZR1442400);中央高校基本科研业务费专项资金资助项目


Synchronous Construction Networked Control System Based on Improved Smith Predictor
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    摘要:

    根据同步施工网络控制系统的控制原理,分析了时变、随机和不确定的网络时延对同步误差控制所产生的影响。针对传统Smith预估器用于多被控对象同步控制时存在的缺陷与不足进行了分析,提出了一种改进型Smith预估器,实现了对网络时延、被控对象纯滞后因子以及同步误差控制器的多重Smith预估补偿,将其从内部反馈回路中彻底消除,预估模型无需预估与在线测量其大小与变化规律。通过基于控制器局域网络(controller area network,简称CAN)的盾构管片拼装机同步网络控制系统,对常规比例积分微分(proportion integral derivative,简称PID)同步网络控制和基于改进型Smith预估器的PID同步网络控制性能进行了仿真对比分析,并通过实验进行了验证。结果表明,常规PID网络控制同步误差为-3~3 mm,基于改进型Smith预估器的PID网络控制同步误差为-1.5~1.5 mm,后者可显著提高同步施工网络控制系统的同步控制性能与精度。

    Abstract:

    Depending on the principle of synchronous construction networked control system (NCS), the effect of time-variant, random and uncertain network delay on the synchronization error is analyzed. Then, the blemish and disadvantage of traditional Smith predictor, which is used for synchronous control of the multiple controlled plants, are also analyzed. An improved Smith predictor is proposed to realize multiple Smith predictive compensation for the network delay, controlled plants with pure delay and the controller of synchronization error, eliminating the delay of feedback path from the control system totally. The prediction model does not contain network delay, so it does not have to be estimated or measured. With the synchronous NCS of segment erector for shield based on CAN (controller area network), the simulation comparison between the conventional PID (proportion integral derivative) networked control and the one based on improved Smith predictor is carried out, and the experiments are performed to evaluate the performance of the proposed method. Finally, the results show that the conventional PID networked control and the one based on improved Smith predictor behave with synchronization error of -3~3mm, -1.5~1.5mm respectively. The latter can improve the performance of the synchronous construction networked control system significantly.

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  • 在线发布日期: 2018-11-02
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