Parameter estimation methods for time-invariant continuous-time systems from dynamical discrete output responses based on the Laplace transforms

IF 3.9 4区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

International Journal of Adaptive Control and Signal Processing Pub Date : 2024-07-03 DOI:10.1002/acs.3871

Kader Ali Ibrahim, Feng Ding

引用次数: 0

Abstract

In industrial process control systems, parameter estimation is crucial for controller design and model analysis. This article examines the issue of identifying parameters in continuous-time models. This article presents a stochastic gradient estimation algorithm and a recursive least squares estimation algorithm for identifying the parameters of continuous systems. It derives the parameter identification model of linear continuous-time systems based on the Laplace transforms of the input and output of the systems. To prove that the techniques given here work, we have included a simulated example.

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基于拉普拉斯变换的动态离散输出响应的时不变连续时间系统参数估计方法

摘要在工业过程控制系统中，参数估计对控制器设计和模型分析至关重要。本文探讨了连续时间模型中的参数识别问题。本文提出了一种随机梯度估计算法和递归最小二乘估计算法，用于识别连续系统的参数。它基于系统输入和输出的拉普拉斯变换，推导出线性连续时间系统的参数识别模型。为了证明这里给出的技术是有效的，我们提供了一个模拟示例。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Adaptive Control and Signal Processing 工程技术-工程：电子与电气

CiteScore

5.30

自引率

16.10%

发文量

163

审稿时长

5 months

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.