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Journals / Electrica / 2018 / Cilt: 18 Sayı: 2

A Self-Tuning PID Control Method for Multi-Input-Multi-Output Nonlinear Systems

Barış BIDIKLI
Journal
Electrica
Pages
218–226
Publication Date
2018-08-03
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The performance of the proposed method is demonstrated via experimental studies.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:DOI: 10.26650/electrica.2018.79181In this study, an artificial neural network (ANN) model-based self-tuning PID control method is proposed for the control of multi-input-multi-output (MIMO) nonlinear systems.
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Key Findings & Data:A single layer, feed-forward ANN structure is trained via input and output data randomly collected from the system and classified as learning, test, and validation data to obtain the system model.
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Conclusion & Impact:The performance of the proposed method is demonstrated via experimental studies.
🔬Design:Modeling & Simulation
📍Scope:2018 Verileri
⚙️Variables:Multi-input-multi-output nonlinear systems · proportional-integral-derivative tuning · proportional-integral-derivative control

Abstract

DOI: 10.26650/electrica.2018.79181In this study, an artificial neural network (ANN) model-based self-tuning PID control method is proposed for the control of multi-input-multi-output (MIMO) nonlinear systems. A single layer, feed-forward ANN structure is trained via input and output data randomly collected from the system and classified as learning, test, and validation data to obtain the system model. The obtained model is utilized in an adaptive PID control scheme in conjunction with two different optimization methods for PID tuning and control. Using this scheme, PID parameters can be tuned to their optimum values and the system can be controlled simultaneously. The performance of the proposed method is demonstrated via experimental studies.

Özet

DOI: 10.26650/electrica.2018.79181In this study, an artificial neural network (ANN) model-based self-tuning PID control method is proposed for the control of multi-input-multi-output (MIMO) nonlinear systems. A single layer, feed-forward ANN structure is trained via input and output data randomly collected from the system and classified as learning, test, and validation data to obtain the system model. The obtained model is utilized in an adaptive PID control scheme in conjunction with two different optimization methods for PID tuning and control. Using this scheme, PID parameters can be tuned to their optimum values and the system can be controlled simultaneously. The performance of the proposed method is demonstrated via experimental studies.

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Cite This Article

BIDIKLI, B. (2018). A Self-Tuning PID Control Method for Multi-Input-Multi-Output Nonlinear Systems. Electrica, Cilt: 18 Sayı: 2, 218–226. https://ajindex.com/articles/1015429

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References (1)

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