Improved EMD-based Oscillation Detection for Mechatronic Closed-Loop Systems

Gurtner, Markus; Zips, Patrik; Atak, Muhammed; Ophey, Julian; Kugi, Andreas

doi:10.1016/j.ifacol.2019.11.703

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…However, the methods still require a huge amount of time and are not so efficient since, in the industrial plant, there are hundreds or thousands of control loops [5]. For example, there is an application of the decomposition method using Empirical Mode Decomposition (EMD) and Fast Adaptive Chirp Mode Decomposition (FACMD) algorithm for oscillation detection in the control loop [6], [7]. Then, a combination of EMD and Delay Vector Variance (DVV) was developed for oscillation detection in chemical industries [8].…”

Section: Introductionmentioning

confidence: 99%

Multi-oscillations Detection for Process Variables Based on K-Nearest Neighbor

Amrullah,

Wardana,

Arif

2023

ELKHA

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In the process industry, a control system is important to ensure the process runs smoothly and keeps the product under predetermined specifications. Oscillations in process variables can affect the decreasing profitability of the plant. It is important to detect the oscillation before it becomes a problem for profitability. Various methods have been developed; however, the methods still need to improve when implemented online for multi-oscillation. Therefore, this research uses a machine learning-based method with the K-Nearest Neighbour (KNN) algorithm to detect multi-oscillation in the control loop, and the detection methods are made to carry out online detection from real plants. The developed method simulated the Tennessee Eastman Process (TEP), and it used Python programming to create a KNN model and extract time series data into the frequency domain. The Message Queuing Telemetry Transport (MQTT) communication protocol has been used to implement as an online system. The result of the implementation showed that two KNN models were made with different window size variations to get the best performance model. The best model for multi-oscillation detection was obtained with an F1 score of 76% for detection.

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Section: Introductionmentioning

confidence: 99%

Multi-oscillations Detection for Process Variables Based on K-Nearest Neighbor

Amrullah,

Wardana,

Arif

2023

ELKHA

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“…In addition, some scholars have designed corresponding control methods to solve the oscillation problem in the system. A method combining empirical mode decomposition with decomposition mode evaluation was proposed in [26]. e method extends the signal preprocessing to improve the robustness and computational efficiency of the system.…”

Section: Introductionmentioning

confidence: 99%

Sliding Mode Control of the Vehicle Speed System Based on LMIs

Gao

Zhang

2021

Complexity

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Due to the nonlinear characteristics of the vehicle speed system, its stability is difficult to control. This paper analyzes the stability and traceability of the vehicle speed system under nonlinear characteristics. A sliding mode control method of the nonlinear system state observation based on linear matrix inequalities (LMIs) is proposed. In the proposed control method, Lyapunov function is used as the control function to track the position and speed of the vehicle speed system in real time. In the design process of the controller, the successive scaling method (SSM) is designed to improve the tracking accuracy. The simulation results demonstrate that the sliding mode control can effectively track the position of the vehicle speed system, which has better stability and traceability for the nonlinear vehicle speed system.

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