Nonlinear Compensation of the Linear Variable Differential Transducer Using an Advanced Snake Optimization Integrated
Qiuxia Fan1, Xinqi Zhang1, Zhuang Wen1
1School of Automation and Software Engineering, Shanxi University, Taiyuan 030006, China.
Sensors (Basel, Switzerland)
|February 26, 2025
Summary
This study introduces an advanced Snake Optimization-Tangential Functional Link Artificial Neural Network (ASO-TFLANN) to enhance the linear range of Linear Variable Differential Transformers (LVDTs). The novel method improves measurement accuracy and extends the operational range for vibration measurement applications.
Area of Science:
- Instrumentation and Measurement
- Artificial Intelligence
- Control Systems
Background:
- Linear Variable Differential Transformers (LVDTs) are crucial for measuring vibration and active isolation.
- LVDT nonlinearities at increased displacements limit their measurement range.
- Existing methods struggle with overfitting and local optima during training.
Purpose of the Study:
- To extend the linear measurement range of LVDTs.
- To develop an advanced optimization and neural network model for LVDT calibration.
- To improve the accuracy and reliability of LVDT measurements.
Main Methods:
- An enhanced Snake Optimization (SO) algorithm incorporating Latin hypercube sampling and Levy flight (ASO) was developed.
- A voltage-displacement test bench was used to collect LVDT data under various excitations.
- The ASO-TFLANN model was trained using collected data to optimize neural network weights.
Main Results:
- The ASO algorithm effectively addressed overfitting and local optima issues common in gradient descent methods.
- Offline simulations and online tests demonstrated a significant expansion of the LVDT's linear range.
- The proposed method successfully reduced error metrics (ϵfs) and improved overall measurement accuracy.
Conclusions:
- The ASO-TFLANN model offers a robust solution for extending LVDT measurement ranges.
- This approach provides a reliable foundation for enhancing LVDT measurement accuracy and reliability.
- The study validates the effectiveness of the ASO algorithm in optimizing complex systems.
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