Networked control of industrial automation systems—a new predictive method
Networked control of industrial automation systems—a new predictive method
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DOI：
10.1007/s00170-011-3416-1
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发表时间：
2011-06
期刊：
影响因子：
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通讯作者：
B. Rahmani;A. Markazi
中科院分区：
文献类型：
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作者：
B. Rahmani;A. Markazi
介绍了一种实时预测不确定网络传输时延的新方法以及一种通过网络对制造和工业工厂进行闭环控制的方法。所提出的延迟预测方法基于多层感知器神经模型。为了最大限度地减少网络第一层神经元的数量，从而减少实时实现中的计算负担，提出了一种确定时延序列马尔可夫阶数的方法。利用预测的延迟和被控对象的零阶保持等效离散时间模型，提出了一种具有实时增益更新策略的时变状态反馈控制算法。使用线性系统的切换定理还导出了闭环稳定性的充分条件。通过两个工业网络案例研究（即驱动纸张运输辊的直流电机和铣床）展示了所提出的方法。仿真研究描述了所提出的方法在控制此类挑战性问题方面的有效性。
A new method for real-time prediction of uncertain network transmission time delays and a method for closed-loop control of manufacturing and industrial plants through networks are introduced. The proposed delay prediction method is based on the multilayer perceptron neural model. In order to minimize the number of neurons in the first layer of the network and hence reducing the computational burden in a real-time implementation, a method for determination of the Markov order of the time delay sequence is presented. Using the predicted delay, and a zero-order hold equivalent discrete-time model of the plant, a time varying state feedback control algorithm with a real-time gain updating strategy is proposed. A sufficient condition for closed-loop stability is also derived using the switching theorem for linear systems. The proposed method is shown, through two industrial networked case studies, namely, a DC motor driving a transportation roller for paper sheets and a milling machine. Simulation studies depict the efficacy of the proposed method in controlling such challenging problems.