# Dung Beetle Optimized Fuzzy PID Algorithm Applied in Four-Bar Target Temperature Control System
## 1 Introduction  
Infrared thermal imagers utilize the principles of infrared technology to measure the infrared radiation energy emitted from the surface of an object and convert the temperature distribution of the object's surface into a vivid and intuitive thermal image. Due to theirrapid response time, wide measurement range, non-contact measurement capability, and visually intuitive results, infrared thermal imagers are extensively used in military, medical, industrial, and other fields [1; 2]. Infrared thermal imagers are not only a key factor in modern military confrontations but have also gained increasing attention in civilian applications. They are now employed in the diagnosis of cancer, the monitoring of diabetes [3; 4], and the surveillance and thermal fault analysis of equipment in power fields [5]. With the widespread application of infrared thermal imagers, higher demands have been placed on these devices. The minimum resolvable temperature difference (MRTD) is a critical standard for evaluating the performance of infrared thermal imaging systems [6]. Therefore, enhancing the detection accuracy of MRTD testers is a key focus in improving the precision of infrared thermal imagers [7]. Currently, the general method for measuring MRTD involves the following steps: a four-bar target with a high aspect ratio of 7:1 is placed against a uniform background, and the temperature difference between the target and the background is either increased or decreased. The temperature difference at which the human eye can just resolve the image of the four-bar target displayed on the system's monitor corresponds to the MRTD value at that specific spatial frequency. To eliminate test errors caused by zero drift, the average of the positive and negative temperature differences is typically taken as the final test result. Traditional four-bar targets are depicted in Figure 1 [8]. In recent years, researchers have proposed improvements to the MRTD testing method. These improvements shift from traditional human visual observation to the use of CCD cameras [9] by measuring the modulation transfer function (MTF), calculating MRTD using the noise equivalent temperature difference (NETD) [10], and employing neural networks [11]. However, compared to traditional methods, objective measurement techniques struggle to achieve higher accuracy. Although these methods reduce labor costs, they are not suitable for high-precision measurements. In related research, the temperature of the four-bar target is easily influenced by external environmental conditions and the temperature of the blackbody, leading to uneven thermal distribution across the target. This unevenness can prevent the achievement of consistent test conditions across the entire target surface, resulting in imprecise test outcomes and failing to accurately reflect the actual performance of the thermal imaging system. This study proposes an improvement to the four-bar target by employing intelligent optimization algorithms to control the temperature at various points on the target, ensuring uniform temperature distribution across the entire target. In recent studies, researchers have conducted in-depth investigations into various intelligent optimization algorithms for control systems. [PERSON] et al. [12] proposed a temperature control system for a vacuum annealing furnace based on a Genetic Algorithm-optimized fuzzy PID (GA-Fuzzy-PID). Compared to traditional control methods, this system shows significant advantages in temperature accuracy, rise time, and overshoot con  
Figure 1: Four-bar target. trol. [PERSON] [13] [PERSON] et al. also proposed a control method combining genetic algorithms with fuzzy PID. However, the high computational complexity of genetic algorithms might lead to insufficient real-time performance. [PERSON] et al. [14] addressed the nonlinearity and hysteresis issues in lychee preservation equipment by introducing a particle swarm optimization-enhanced fuzzy PID (PSO-Fuzzy PID) control method.