Novel Methods for Nonlinear Control and Optimization: Theory and Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: 15 April 2025 | Viewed by 691

Special Issue Editors


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Guest Editor
Department of Electrical Energy Engineering, Keimyung University, 1095, Dalgubeol-daero, Dalseo-gu, Daegu 42601, Republic of Korea
Interests: system control; nonlinear control; adaptive control; reinforcement learning and optimization

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Guest Editor
Department of Electrical Energy Engineering, Keimyung University, 1095, Dalgubeol-daero, Dalseo-gu, Daegu 42601, Republic of Korea
Interests: electric machine drives control; power conversion system control; grid-connected systems; renewable power generation systems
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Special Issue Information

Dear Colleagues,

Nonlinear control and optimization methods are being increasingly applied to a variety of modern engineering problems. These methods offer significant advantages in managing complex systems, particularly when linear approaches fall short due to the inherent nonlinear nature of many real-world systems. This Special Issue aims to present and disseminate the latest advancements in the theory, analysis, design, and applications of novel methods for nonlinear control and optimization.

We invite high-quality contributions that discuss innovative approaches and practical implementations. Papers are encouraged to cover theoretical developments, case studies, as well as applications in fields such as robotics, power systems, aerospace, automotive engineering, and beyond.

Topics of interest for this Special Issue include but are not limited to the following:

  • Advanced nonlinear control methods;
  • Adaptive control;
  • Robust and optimal control techniques;
  • Model predictive control;
  • Intelligent control systems and machine learning applications in control;
  • Control applications in robotics, power systems, and aerospace engineering;
  • Optimization algorithms for nonlinear systems.

Prof. Dr. Sesun You
Prof. Dr. Yeongsu Bak
Guest Editors

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Keywords

  • nonlinear control
  • optimization methods
  • model predictive control
  • adaptive and robust control
  • optimization algorithms
  • control applications in engineering

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Published Papers (1 paper)

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Research

20 pages, 5415 KiB  
Article
High-Precision Main Shaft Displacement Measurement for Wind Turbines Using an Optimized Position-Sensitive Detector
by Weitong Zhang, Lingyun Wang, Guangxi Li, Huicheng Zheng and Chengwei Pang
Electronics 2024, 13(24), 5055; https://doi.org/10.3390/electronics13245055 - 23 Dec 2024
Viewed by 509
Abstract
The main shaft of a wind turbine is a critical component that ensures the normal operation of the turbine, and its axial displacement directly impacts its efficiency and safety. The inaccurate measurement of axial displacement may lead to severe issues such as shaft [...] Read more.
The main shaft of a wind turbine is a critical component that ensures the normal operation of the turbine, and its axial displacement directly impacts its efficiency and safety. The inaccurate measurement of axial displacement may lead to severe issues such as shaft fractures, causing turbine shutdowns. Correcting measurement errors related to axial displacement is essential to prevent potential accidents. This study proposes an improved error correction method for measuring the axial displacement of wind turbine main shafts. Using a position-sensitive detector (PSD) and laser triangulation, the axial and radial displacements of the main shaft are measured to address environmental interference and cost constraints. Additionally, a Sparrow Search Algorithm- Backpropagation (SSA-BP) model is constructed based on operational data from the wind turbine’s main shaft to correct the system’s nonlinear errors. The Sparrow Search Algorithm (SSA) is employed to optimize the weights and thresholds of the Backpropagation (BP) neural network, enhancing prediction accuracy and model stability. Initially, a main shaft displacement measurement system based on a precision displacement stage was developed, and system stability tests and displacement measurement experiments were conducted. The experimental results demonstrate that the system stability error is ±0.025 mm, which is lower than the typical error of 0.05 mm in contact measurement. After model correction, the maximum nonlinear errors of the axial and radial displacement measurements are 0.83% and 1.29%, respectively, both of which are lower than the typical measurement error of 2% in contact measurements. This indicates that the proposed model can reliably and effectively correct the measurement errors. However, further research is still necessary to address potential limitations, such as its applicability in extreme environments and the complexity of implementation. Full article
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