Abstract

This paper discusses a strategy for estimating Hammerstein nonlinear systems in the presence of measurement noises for industrial control by applying filtering and recursive approaches. The proposed Hammerstein nonlinear systems are made up of a neural fuzzy network (NFN) and a linear state`-space model. The estimation of parameters for Hammerstein systems can be achieved by employing hybrid signals, which consist of step signals and random signals. First, based on the characteristic that step signals do not excite static nonlinear systems, that is, the intermediate variable of the Hammerstein system is a step signal with different amplitudes from the input, the unknown intermediate variables can be replaced by inputs, solving the problem of unmeasurable intermediate variable information. In the presence of step signals, the parameters of the state-space model are estimated using the recursive extended least squares (RELS) algorithm. Moreover, to effectively deal with the interference of measurement noises, a data filtering technique is introduced, and the filtering-based RELS is formulated for estimating the NFN by employing random signals. Finally, according to the structure of the Hammerstein system, the control system is designed by eliminating the nonlinear block so that the generated system is approximately equivalent to a linear system, and it can then be easily controlled by applying a linear controller. The effectiveness and feasibility of the developed identification and control strategy are demonstrated using two industrial simulation cases.

中文翻译：

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使用工业控制的滤波和递归方法估计带有噪声的 Hammerstein 非线性系统

摘要

本文讨论了一种通过应用滤波和递归方法在存在工业控制测量噪声的情况下估计 Hammerstein 非线性系统的策略。所提出的 Hammerstein 非线性系统由神经模糊网络（NFN）和线性状态空间模型组成。Hammerstein 系统的参数估计可以通过采用由阶跃信号和随机信号组成的混合信号来实现。首先，根据阶跃信号不激励静态非线性系统的特点，即Hammerstein系统的中间变量是与输入不同幅值的阶跃信号，可以用输入代替未知的中间变量，求解问题不可测量的中间变量信息。在存在阶跃信号的情况下，使用递归扩展最小二乘 (RELS) 算法来估计状态空间模型的参数。此外，为了有效应对测量噪声的干扰，引入了数据滤波技术，并制定了基于滤波的RELS，用于利用随机信号估计NFN。最后，根据Hammerstein系统的结构，通过消除非线性模块来设计控制系统，使生成的系统近似等效于线性系统，从而可以方便地应用线性控制器进行控制。使用两个工业仿真案例证明了所开发的识别和控制策略的有效性和可行性。