Abstract: Based on the MATLAB/Simulink platform, a simulation model of a fuzzy adaptive PID system is established and applied to the control process of boiler drum water level. The specific implementation method of the simulation is given, and an interference signal is added during the system operation. The results show that the system has good dynamic performance and strong anti-interference performance, proving the effectiveness of the proposed method. Keywords: PID Fuzzy self-tunning PID controller in the boiler drum level based on MATLAB / simulink Fuzzy self-tunning PID system is built up and applied to control boiler drum level.The detailed method of emulation is given. The interference signal is added in the operation of system.The result shows good dynamic performance and stronger anti-interference function. It proves the approach proposed in this paper is effective. Introduction PID control is a perennial favorite in the field of control, still accounting for over 90% of loops in industrial process control. It boasts advantages such as simple structure, strong adaptability, and good robustness. However, due to limitations in control parameter tuning, it struggles to achieve satisfactory control results for nonlinear and large-time-delay systems. Fuzzy control in intelligent control simulates human logical thinking, does not rely on a precise mathematical model of the controlled object, and only requires the experience and operational data of professional technicians, demonstrating excellent control performance for complex systems. This paper combines the above two control methods to design a fuzzy adaptive PID system, enabling online adjustment of PID control parameters. It exhibits strong adaptability to complex systems, and utilizes the intuitive and convenient modeling capabilities of MATLAB/Simulink to conduct simulation experiments on boiler drum water level control. [b]2 Design of Fuzzy Adaptive PID Control System 2.1 Structure of Fuzzy Adaptive PID Control System[/b] The structure of the fuzzy adaptive PID control system is shown in Figure 1. As shown in the figure, the system consists of two parts: conventional PID control and fuzzy inference control. The deviation e and the rate of change of deviation ec (de/dt) are used as inputs to the fuzzy controller. The PID parameters are adaptively adjusted according to the fuzzy control rules to meet the requirements of control parameters for different e and ec. The calculation formulas for the self-adjusting PID parameters of this system are as follows: kp=k′p+△kp (2.1) ki=k′i+△ki (2.2) kd=k′d+△kd (2.3) Where k′p, k′i, and k′d are the initial values ​​of the PID parameters, △kp, △ki, and △kd are the outputs of the fuzzy controller, and kp, ki, and kd are the final output control parameter values. 2.2 Fuzzy Controller Structure In the MATLAB command window, type fuzzy to bring up the FIS editor interface, where you can easily edit the required fuzzy inference system. Select the controller type as Mamdani, the AND method as min, the OR method as max, the Implication method as min, the Aggregation method as max, and the Defuzzification method as centroid (centroid averaging) [1]. As shown in Figure 1, the fuzzy controller has two inputs (e, ec) and three outputs (k[sub]p[/sub], k[sub]i[/sub], k[sub]d[/sub]). Open the FIS editor drop-down menu edit, and select the number of input and output variables in Add Variable. 2.2.1 Membership Function Assume that both input and output variables use triangular membership function curves with a universe of discourse of [-6, +6] and fuzzy subsets {negative large, negative medium, negative small, zero, positive large, positive medium, positive large}, corresponding to {NB, NM, NS, O, PS, PM, PB} respectively. Double-clicking the icons of each input and output variable opens the membership function editor, allowing for the above settings. 2.2.2 Fuzzy Control Rules Based on relevant literature and experience, the tuning principles for parameters kp, ki, and kd are summarized for different e and ec values: A. When |e| is large, to quickly eliminate deviation and improve response speed, a larger kp should be chosen. Simultaneously, to prevent excessive overshoot and oscillation, a smaller kd can be chosen, and ki can be set to 0. B. When e·ec > 0, the controlled variable changes in the direction deviating from the given value. If |e| is large, take a large kp, a small ki, and a medium kd; if |e| is small, take a medium kp, a large ki, and a small kd to improve the steady-state performance of the system and avoid oscillations. C. When e·ec < 0, the controlled variable changes in the direction closer to the given value. If |e| is large, take a medium kp, a small ki, and a medium kd to improve dynamic and steady-state performance; if |e| is small, take a small kp, a large ki, and a small kd. D. The magnitude of ec indicates the rate of change of the deviation. The larger ec is, the smaller the value of kp and the larger the value of ki, and vice versa. The above analysis yields a fuzzy control rule table. Fuzzy control rules can be entered in the Rules section of the edit panel, in the form of: If (e is NB) and (ec is NB) then (kp is PB) (ki is NB) (kd is PS) If (e is NB) and (ec is NM) then (kp is PB) (ki is NB) (kd is NS) If (e is NB) and (ec is NS) then (kp is PM) (ki is NM) (kd is NB) If (e is NB) and (ec is ZO) then (kp is PM) (ki is NM) (kd is NB) If (e is NB) and (ec is PS) then (kp is PS) (ki is NS) (kd is NB) …… At this point, a file named gw.fis is created, completing the overall design of the fuzzy controller structure. [b]2.3 System Simulation Analysis Based on MATLAB/Simulink Environment 2.3.1 System Model Based on MATLAB/Simulink[/b] The incremental formula commonly used in conventional PID control is: △u(k) = k[sub]p[/sub]△e(k) + k[sub]i[/sub]e(k) + k[sub]d[/sub]△e(k) - △e(k-1)] (2.4) Combining equations 2.1, 2.2, and 2.2, a PID simulation module as shown in Figure 2 can be established in the MATLAB/Simulink environment and encapsulated into a PID Subsystem. A boiler drum with a steam supply of 120t/h is selected as the controlled object, and its transfer function between feedwater flow and water level is: G(s) = 0.0529/8.5s[sup]2[/sup] + s The Simulink simulation model of the fuzzy adaptive PID control system is shown in Figure 3. Double-click the Fuzzy Logic Controller module, enter the filename gw.fis created above in the parameters field, and enter the following two commands in the MATLAB command window: readfis; fismat=readfis('gw') to read the fis file into the workspace, thus establishing the connection between Simulink and the fis file and completing the system simulation. 2.3.2 Simulation Result Analysis The quantization factors ke = 0.3 and kec = 0.1 were set, and the scaling factors for kp, ki, and kd were set to k1 = 0.8, k2 = 0.005, and k3 = 25, respectively. The initial values ​​of the PID parameters were set to k′p = 300, k′i = 0.3, and k′d = 280. The simulation time was set to 40 seconds, a unit step signal was applied, and a 5.0 (500%) disturbance was added at the 10th second. The final response curves of the conventional PID and the fuzzy adaptive PID are shown in Figures 4 and 5, respectively. [align=center] Figure 4: Response curve of conventional PID control system[/align] [align=center] Figure 5: Response curve of fuzzy adaptive PID control system[/align] It can be seen from the curve that the performance indicators of fuzzy adaptive PID control are better than those of conventional PID control. It has the characteristics of fast response, small overshoot, and fast transition time, showing good dynamic performance and steady-state accuracy, and strong anti-interference ability. 3 Conclusion This paper combines fuzzy control with conventional PID control to form an intelligent control system with parameter adaptive function. The system simulation model was built in the MATLAB/Simulink environment. The boiler drum water level was selected as the simulation object. The simulation results show that the fuzzy adaptive PID control method is feasible for boiler drum water level control. The offline simulation experiment reduces the manpower and material resources for debugging and provides a theoretical basis for actual production. References: [1] Liu Bing, Li Wen, Ding Mingyan. Design and simulation of fuzzy PID control system based on MATLAB[J]. Instrument and Meter User, 2006, 13(2). [2] Yang Yongmei, Chen Ning. Design and simulation of fuzzy self-tuning PID parameter controller based on MATLAB[J]. Microcomputer Information, 2005, 21(12). * [3] Liu Zhiyuan, Cai Ken, Bai Yanli. Design of Fuzzy-pid controller based on Simulink [J]. Journal of Shaoguan University, 2005, 26(9). * [4] Song Ziwei, Chen Sizhong, Yang Lin. MATLAB simulation analysis of fuzzy-PID control. Science and Technology Information [J], 2006, 3 [5] Chen Ping, Wang Yongchu, Chen Xiaoyun. A boiler drum water level control method based on fuzzy pid control [J]. Journal of Fujian University of Technology, 2006, 4(1). Email: [email protected] Address: P.O. Box 162, Graduate School, Anhui Agricultural University