# Dung Beetle Optimized Fuzzy PID Algorithm Applied in Four-Bar Target Temperature Control System
## 1 Introduction
The results indicated that the PSO-Fuzzy PID scheme significantly reduced disturbance adjustment time and eliminated overshoot, exhibiting superior control performance, although it might fall into local optima during computation, leading to inaccurate parameter adjustments. [PERSON] et al. [15] introduced an improved sparrow search algorithm (ISSA) to optimize PID parameters in the temperature control process of a continuous stirred tank reactor (CSTR). Experimental results showed improved transient and steady-state performance of the reactor temperature control with good precision and robustness, although the convergence was poor after enhancing the capability to escape local minima. [PERSON] [16] [PERSON] et al. proposed a novel hybrid strategy combining the spiral dynamics algorithm (SDA) with the bacterial foraging algorithm (BFA) to design a fuzzy controller for flexible manipulators, but its slow convergence rate made it unsuitable for real-time systems. [PERSON] et al. [17] designed a temperature control system for infant radiant warmers using a Kalman filter-based fuzzy PID (KFPID) algorithm. Experimental results demonstrated shorter adjustment times, nearly zero overshoot, and smaller steady-state errors compared to control systems without this algorithm, particularly showing effectiveness in noise suppression. [PERSON] et al. [18] introduced a novel hybrid controller that combines the fuzzy PID with PID optimized by the whale optimization algorithm (WOA), showing excellent performance in frequency regulation in hybrid power systems, though the method has a slow convergence rate and is sensitive to initial settings. [PERSON] et al. [19] proposed a fuzzy logic power system stabilizer (FLPSS) based on the grey wolf optimization algorithm (GWO). Compared to traditional stabilizers and optimization algorithms, it provided faster system response time and higher damping of oscillations, although its robustness was less satisfactory. [PERSON] et al. [20] proposed a novel estimation algorithm--the robust extended Kalman filter--to estimate the fundamental phase component of the reference current and employed a novel adaptive fuzzy PID controller to generate the maximum value of the reference current. All controller parameters were optimized using an improved football game optimization (IFGO) technique. Additionally, the sliding mode control (SMC) technique was used to generate switching pulses for the voltage source converter (VSC). [PERSON] et al. [21] introduced a gas distribution system based on a fuzzy PID controller, where the system's mathematical model was built using the step response method, and the gas distribution control process was optimized. However, this method is subject to the influence of human factors on parameter adjustments. To address these challenges, this paper improves the four-bar target by installing thermoelectric coolers (TECs) and thin-film platinum resistors on its surface to lower the central temperature and elevate the peripheral temperature, thereby maintaining consistent temperature across the entire plane. A high-temperature uniformity target provides an accurate and consistent thermal field, which allows for more precise measurement of the MRTD value of infrared thermal imagers. In controlling the temperature of the four-bar target, this paper introduces the dung beetle algorithm for online optimization of quantization and proportionality factors, seeking optimal solutions for each parameter. This significantly enhances the accuracy and reliability of MRTD detection, thereby positively impacting the performance evaluation of thermal imaging products. The dung beetle algorithm is a heuristic optimization algorithm based on biomimetic principles and inspired by the behavior of dung beetles when searching for food and shelter. Compared to other algorithms, the dung beetle algorithm first utilizes swarm intelligence to search the entire solution space, which helps in discovering the global optimum and improving the overall performance of the control system.