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

Organic Light-Emitting Diode (OLED) is a high-performance display technology. Its performance and lifespan are extremely sensitive to the operating temperature. The existing temperature control methods, such as the traditional Proportional-Integral-Derivative (PID) controller, are difficult to meet the requirements of OLED for precise temperature control, especially in systems with significant nonlinear and time-varying characteristics. To solve this problem, the study proposes an improved PID controller based on the Long Short-Term Memory (LSTM) optimized by the Whale Optimization Algorithm (WOA). This method combines the global optimization ability of WOA and the timing analysis ability of LSTM. By optimizing the parameters of the PID controller, the accuracy and adaptability of temperature control are improved. Meanwhile, the effectiveness of the proposed controller is verified by constructing a thermodynamic model and combining experimental data. In the experimental results, compared with the traditional PID controller, the overshoot of the WOA-LSTM-PID controller was reduced from 8.5°C to 0.3°C, the steady-state error was reduced from 1.2°C to 0.2°C, the regulation time was shortened from 42.5 seconds to 20.2 seconds, and the response time was shortened from 70.5 seconds to 21.9 seconds. Furthermore, the root mean square error has been reduced from 5.23°C of the traditional PID to 0.78°C. The research results show that the WOA-LSTM-PID controller can significantly improve the accuracy and stability of OLED temperature control, while reducing the regulation time and response time. This controller effectively addresses the nonlinear and time-varying characteristics in OLED temperature control by optimizing the PID parameters. The innovation of the research lies in the combination of the WOA and the LSTM network. By optimizing the parameters of the PID controller, high-precision control of the OLED temperature has been achieved. This study not only proposes a new theoretical optimization method but also verifies its significant performance improvement in experiments. Furthermore, this method has strong universality and can be applied to other temperature-sensitive systems.