# A PID Parameter Tuning Method Based on the Improved QUATRE Algorithm
## 1 Introduction  
The PID controller's function is to determine the steady-state error value of the system from the actual given value and the actual output value of the control system. The steady-state error is linearly calculated by proportion, integral, and differential to form the control quantity. The control effect of the PID controller on the controlled object can be changed by changing the values of three kinds of numbers. Due to this, in many cases, PID controller parameter tuning is indispensable in the control system, as it is an important control method to achieve the purpose of control. The traditional PID controller parameter tuning method is manual tuning, through calculations to obtain PID controller parameters, and then fine-tuning to achieve a more ideal control effect. The traditional PID controller parameter tuning method has a good effect on the linear time-invariant system. When facing the complex controlled object with nonlinear and time delay characteristics, the artificial calculation is large and time-consuming and the parameter error is large. Even if the parameter control system is fine-tuned, it is almost impossible to achieve the desired control effect. With the continuous development of industrial control technology and the large-scale use of power electronic devices, the control objects of actual industrial control present high-order, time-delay, and nonlinear characteristics. It is difficult to achieve the optimal tuning of P1D parameters with traditional PID parameter tuningmethods. For complex control objects, many scholars apply swarm intelligence algorithms to PID parameter tuning. The development of information and computing technology and the penetration of other related disciplines promote the research of control science and engineering, and intelligent control has become an important trend. To achieve a better control effect of the PID controller in complex controlled objects, this paper proposes a method of combining the new improved algorithm with PID parameters and proves its feasibility and superiority. Intelligent optimization algorithm is a general term for a large class of algorithms and an important part of computational intelligence [1], including the evolutionary computing method, the swarm intelligence algorithm based on biological group behavior, and a random search algorithm based on physical process simulation [2]. For a long time, finding solutions to a problem has been one of the goals of human production and life. There are many fields that use an optimization algorithm in combination with, e.g., a control system to find the best control scheme [3]; image processing; wireless sensor network [4; 5]; information hiding [6; 7]; and in real life, it can be applied to vehicle routing problems [8]. The optimization algorithm was applied in image and network segmentation in [5; 7]. The algorithm was also used for sensor ontology meta-matching in [9]. When the problem to be solved has a non-unique feasible solution, obtaining the optimal solution of a certain performance index through optimization is essential. The process of obtaining the optimal solution is the process of finding the best solution. In summary, an intelligent optimization algorithm is an excellent and practical tool. To combine the swarm intelligence optimization algorithm with PID control, we must first determine the similarities between them. The algorithm must find the best solution to the equation, and the uncertainty of the control is the reason why we need to determine the best control state. Parameters also have a variety of scheme combinations. We need to match the PID control parameters with the fitness function of the algorithm to find the best control scheme. In the algorithm, all individuals are in parameter combination with the PID controller. The fitness values of all individuals were calculated by using the performance index function.