Improvement analysis of organic light emitting diode temperature control by integrating whale algorithm in PID...

Area of Science:

• Materials Science and Engineering
• Control Systems and Automation
• Artificial Intelligence and Machine Learning

Background:

• Organic Light-Emitting Diodes (OLEDs) are sensitive to operating temperature, impacting performance and lifespan.
• Traditional Proportional-Integral-Derivative (PID) controllers struggle with the nonlinear and time-varying characteristics of OLED temperature control.
• Precise temperature management is crucial for ensuring the reliability and longevity of OLED displays.

Purpose of the Study:

• To develop an advanced temperature control strategy for OLEDs that overcomes the limitations of conventional methods.
• To enhance the accuracy, adaptability, and stability of OLED temperature control systems.
• To introduce a novel controller combining global optimization and time-series analysis capabilities.

Main Methods:

• Proposed an improved PID controller integrating Long Short-Term Memory (LSTM) neural networks.
• Optimized PID controller parameters using the Whale Optimization Algorithm (WOA) for enhanced global search.
• Validated the controller's effectiveness through a thermodynamic model and experimental data analysis.

Main Results:

• The WOA-LSTM-PID controller significantly reduced overshoot (8.5°C to 0.3°C) and steady-state error (1.2°C to 0.2°C).
• Achieved substantial reductions in regulation time (42.5s to 20.2s) and response time (70.5s to 21.9s).
• Demonstrated a marked decrease in root mean square error from 5.23°C (traditional PID) to 0.78°C.

Conclusions:

• The WOA-LSTM-PID controller offers superior accuracy and stability for OLED temperature control.
• The proposed method effectively addresses nonlinear and time-varying system dynamics.
• The combined WOA and LSTM approach presents a novel, universally applicable solution for temperature-sensitive systems.