Rapid parameter identification of linear time-delay system from noisy frequency domain data

Liu, Guang; Yu, Minli; Wang, Li; Yin, Zhiyi; Liu, Jike

doi:10.1016/j.apm.2020.03.015

Cited by 12 publications

(5 citation statements)

References 34 publications

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“…Meanwhile, they reconstructed the observation and augmented data to obtain the explicit expression of the delay parameter. To achieve an effective system control strategy and accurate response prediction, Liu et al [ 19 ] proposed a new method to identify the parameters of linear time-delay differential systems by analyzing the frequency domain response of complex systems. To solve the influence of time delay on HVAC systems, Li et al [ 20 ] introduced transfer entropy and proposed a model-free identification method based on the information theory framework.…”

Section: Introductionmentioning

confidence: 99%

Multi-Delay Identification of Rare Earth Extraction Process Based on Improved Time-Correlation Analysis

Liu

Yang

et al. 2023

Sensors

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The rare earth extraction process has significant time delay characteristics, making it challenging to identify the time delay and establish an accurate mathematical model. This paper proposes a multi-delay identification method based on improved time-correlation analysis. Firstly, the data are preprocessed by grey relational analysis, and the time delay sequence and time-correlation data matrix are constructed. The time-correlation analysis matrix is defined, and the H∞ norm quantifies the correlation degree of the data sequence. Thus the multi-delay identification problem is transformed into an integer optimization problem. Secondly, an improved discrete state transition algorithm is used for optimization to obtain multi-delay. Finally, based on an Neodymium (Nd) component content model constructed by a wavelet neural network, the performance of the proposed method is compared with the unimproved time delay identification method and the model without an identification method. The results show that the proposed algorithm improves optimization accuracy, convergence speed, and stability. The performance of the component content model after time delay identification is significantly improved using the proposed method, which verifies its effectiveness in the time delay identification of the rare earth extraction process.

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Section: Introductionmentioning

confidence: 99%

Multi-Delay Identification of Rare Earth Extraction Process Based on Improved Time-Correlation Analysis

Liu

Yang

et al. 2023

Sensors

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“…14 However, the physical meanings of the original states are often not preserved by the projection approach, making it difficult to handle state constraints. Data-based model identification methods are often used to approximate complex nonlinear models 8,[15][16][17] with no first-principle knowledge regarding the system required. However, it may be challenging to determine an appropriate model structure, which has a significant impact on the final modeling performance.…”

Section: Introductionmentioning

confidence: 99%

Model predictive control of agro‐hydrological systems based on a two‐layer neural network modeling framework

Huang

Liu

Huang

2023

Adaptive Control & Signal

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Water scarcity is an urgent issue to be resolved and improving irrigation water-use efficiency through closed-loop control is essential. The complex agro-hydrological system dynamics, however, often pose challenges in closed-loop control applications. In this work, we propose a two-layer neural network (NN) framework to approximate the dynamics of the agro-hydrological system. To minimize the prediction error, a linear bias correction is added to the proposed model. The model is employed by a model predictive controller with zone tracking (ZMPC), which aims to keep the root zone soil moisture in the target zone while minimizing the total amount of irrigation. The performance of the proposed approximation model framework is shown to be better compared to a benchmark long-short-term-memory model for both open-loop and closed-loop applications. Significant computational cost reduction of the ZMPC is achieved with the proposed framework. To handle the tracking offset caused by the plant-model-mismatch of the proposed NN framework, a shrinking target zone is proposed for the ZMPC. Different hyper-parameters of the shrinking zone in the presence of noise and weather disturbances are investigated, of which the control performance is compared to a ZMPC with a time-invariant target zone.

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“…Data-based model identification method is often used to approximate complex nonlinear models [8,15,16,17] with no first-principle knowledge regarding the system required. The drawback, however, is that traditional model identification approaches require the model structure to be selected ahead of time, which may significantly affect the model performance.…”

Section: Introductionmentioning

confidence: 99%

Model predictive control of agro-hydrological systems based on a two-layer neural network modeling framework

Huang¹,

Liu²,

Huang³

2022

Preprint

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Water scarcity is an urgent issue to be resolved and improving irrigation water-use efficiency through closed-loop control is essential. The complex agro-hydrological system dynamics, however, often pose challenges in closed-loop control applications. In this work, we propose a twolayer neural network (NN) framework to approximate the dynamics of the agro-hydrological system. To minimize the prediction error, a linear bias correction is added to the proposed model. The model is employed by a model predictive controller with zone tracking (ZMPC), which aims to keep the root zone soil moisture in the target zone while minimizing the total amount of irrigation. The performance of the proposed approximation model framework is shown to be better compared to a benchmark long-short-term-memory (LSTM) model for both open-loop and closed-loop applications. Significant computational cost reduction of the ZMPC is achieved with the proposed framework. To handle the tracking offset caused by the plantmodel-mismatch of the proposed NN framework, a shrinking target zone is proposed for the ZMPC. Different hyper-parameters of the shrinking zone in the presence of noise and weather disturbances are investigated, of which the control performance is compared to a ZMPC with a time-invariant target zone.

show abstract

Rapid parameter identification of linear time-delay system from noisy frequency domain data

Cited by 12 publications

References 34 publications

Multi-Delay Identification of Rare Earth Extraction Process Based on Improved Time-Correlation Analysis

Multi-Delay Identification of Rare Earth Extraction Process Based on Improved Time-Correlation Analysis

Model predictive control of agro‐hydrological systems based on a two‐layer neural network modeling framework

Model predictive control of agro-hydrological systems based on a two-layer neural network modeling framework

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