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引用次数: 0
摘要
本文研究了测量噪声下多输入多输出(MIMO)哈默斯坦非线性系统的参数学习方案,该方案是利用相关分析和数据过滤技术得出的。所提出的 MIMO Hammerstein 耦合系统包括一个由神经模糊模型(NFM)建模的静态非线性子系统和一个由额外输入自回归移动平均(ARMAX)模型建立的动态线性子系统。为了学习 MIMO Hammerstein 系统的未知参数,设计了组合信号,以实现非线性子系统识别与线性子系统识别的分离。首先,分析非线性系统中可分离信号的相关特性,然后利用相关分析估计线性子系统的参数,从而解决汉默施泰因系统中中间变量无法测量的问题。其次,引入数据滤波技术,推导出基于数据滤波的递归最小二乘法技术,用于学习非线性子系统参数,从而降低移动平均噪声的影响,提高参数估计的精度。最后,通过数值模拟和非线性 pH 过程证明了所提识别方案的有效性和可行性。
Parameter learning of multi‐input multi‐output Hammerstein system with measurement noises utilizing combined signals
In this article, the parameter learning scheme for the multi‐input multi‐output (MIMO) Hammerstein nonlinear systems under measurement noises is studied, which is derived by exploiting the correlation analysis and data filtering technique. The coupled MIMO Hammerstein system presented involves a static nonlinear subsystem modeled by neural fuzzy model (NFM), and a dynamic linear subsystem established by autoregressive moving average with extra input (ARMAX) model. To learn the unknown parameter of the MIMO Hammerstein system, the combined signals are designed to realize that identification of the nonlinear subsystem is separated from that of linear subsystem. First, the correlation properties of separable signals in a nonlinear system are analyzed, then the parameters of the linear subsystem are estimated utilizing correlation analysis, which can deal with the issue of unmeasured intermediate variable in the Hammerstein system. Second, the data filtering technique is introduced to derive the data filtering‐based recursive least squares technique for learning the nonlinear subsystem parameter, which can reduce the impact of the moving average noise and improve the precision of parameter estimation. Finally, the effectiveness and feasibility of the proposed identification scheme is proved by numerical simulation and nonlinear pH process.
期刊介绍:
The International Journal of Adaptive Control and Signal Processing is concerned with the design, synthesis and application of estimators or controllers where adaptive features are needed to cope with uncertainties.Papers on signal processing should also have some relevance to adaptive systems. The journal focus is on model based control design approaches rather than heuristic or rule based control design methods. All papers will be expected to include significant novel material.
Both the theory and application of adaptive systems and system identification are areas of interest. Papers on applications can include problems in the implementation of algorithms for real time signal processing and control. The stability, convergence, robustness and numerical aspects of adaptive algorithms are also suitable topics. The related subjects of controller tuning, filtering, networks and switching theory are also of interest. Principal areas to be addressed include:
Auto-Tuning, Self-Tuning and Model Reference Adaptive Controllers
Nonlinear, Robust and Intelligent Adaptive Controllers
Linear and Nonlinear Multivariable System Identification and Estimation
Identification of Linear Parameter Varying, Distributed and Hybrid Systems
Multiple Model Adaptive Control
Adaptive Signal processing Theory and Algorithms
Adaptation in Multi-Agent Systems
Condition Monitoring Systems
Fault Detection and Isolation Methods
Fault Detection and Isolation Methods
Fault-Tolerant Control (system supervision and diagnosis)
Learning Systems and Adaptive Modelling
Real Time Algorithms for Adaptive Signal Processing and Control
Adaptive Signal Processing and Control Applications
Adaptive Cloud Architectures and Networking
Adaptive Mechanisms for Internet of Things
Adaptive Sliding Mode Control.