基于植物参数化的δ-模型自适应算法

Zhao Feng, Liu Weiguo
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引用次数: 0

摘要

研究了给定条件下非线性工业过程的自适应控制问题。本文提出了一种改进的闭环辨识迭代方案,并设计了基于植物参数化的delta模型自适应控制器。这种修改使得仅使用一个系数就可以识别整个工厂,而无需降低新工厂模型的阶数。此外,它没有使用最小二乘算法,而是使用了一个简单的识别公式。此外,引入delta模型有助于解决采样间隔缩短时离散模型的数值不稳定性问题。数字仿真结果表明,该算法具有良好的控制效果
本文章由计算机程序翻译,如有差异,请以英文原文为准。
δ-model Adaptive Algorithm Based on Plant-Parameterization
Focusing on some adaptive control problems of nonlinear industrial processes under given conditions. This paper proposes a modified iterative scheme of the closed-loop identification and designs delta-model adaptive controller based on plant-parameterization. The modification enables to identify the whole plant using only one coefficient and without the necessity of reducing the order of a new plant model. Moreover, instead of using a least squares algorithm, only a simple formula for identification is used. In addition, introduction of delta-models helps to cope with numerical instabilities of discrete models occurring when a sampling interval is being shortened. Digital simulation demonstrates that the proposed algorithm brings about good control results
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