# Research on the Autonomous Control Technology Used in the Slurry Mixing System of Cementing Units
###### Abstract  
C cementing is a critical link in oil and gas exploitation, in which slurry density control is particularly important. In this study, we examined a slurry mixing control system in order to solve the problem of time delays in the mixing system. The model of a slurry mixing system was built in accordance with the system's structure. A Smith fuzzy PID (proportion integration differentiation) composite control solution is proposed herein, and the simulation results show that the adjustment time and overshoot are lower than those of the conventional PID control and Smith predictive compensation control. A genetic algorithm is utilized to optimize the quantization factor and scale factor of the [PERSON] fuzzy PID controller. Following optimization, the rise time of the controller was found to be 0.45 s, which represents a decrease of 35.9%, the overshoot was reduced by 0.4%, and the stabilization time was reduced by 36.6%. Afterward, we built a cementing slurry mixing simulation experimental platform, and experiments were used to verify the feasibility and superiority of the [PERSON] fuzzy PID controller optimized by the genetic algorithm in comparison with the conventional controllers. The study results thus provide a scientific basis for the engineering application of the autonomous control technology of the slurry mixing system in cementing units. c 2024 14 3568 2024 1 control, in their study, [PERSON] et al. used particle swarm optimization to optimize the parameters of the fuzzy controller and improve the dynamic performance of the controller [4]. [PERSON] et al. proposed a main steam pressure control technology based on a mismatch compensation Smith RBF (radial basis function) neural network, which was found to be satisfactory in terms of stability and anti-interference ability [5]. [PERSON] et al. achieved multi-objective optimization of Smith prediction model parameters, effectively overcoming the nonlinearity of the system [6]. [PERSON] proposed a PID Smith control algorithm based on particle swarm and verified that the controller had strong robustness and adaptability [7]. [PERSON] et al. proposed an improved genetic algorithm-optimized fuzzy controller and proved that the system's rapidity and stability were improved following optimization [8]. [PERSON] et al. used PSO to optimize a water and fertilizer device based on [PERSON] fuzzy PID control, and the optimized system was found to have a faster response speed and higher accuracy [9]. In one study, a neural network was introduced to optimize the gain of the PID controller and reduce the adjustment time and steady-state error [10]. In another study, a multi-layer controller was designed to improve the learning ability and flexibility of a fuzzy neural network, and the authors conducted simulation and verification of the controller [11]. In a different study, a class of self-organizing intelligent controllers (SOICs) for nonlinear systems was proposed, the structure and parameters of the SOICs were updated by an adaptive projection algorithm, and the superior control performance of the SOICs was verified [12]. The authors of another study improved the fuzzy self-tuning PID (FA-PID) method based on the rate of change, and the adjusted control parameters were calculated according to the predicted output of the control system. The authors verified the performance of the controller, such as its fast control speed, strong anti-interference ability, limited overshoot, and small steady-state error [13]. The authors then proposed a fault diagnosis framework for autonomous vehicles based on hybrid data analysis and fuzzy proportional-integral-derivative (PID) control. The effectiveness of the proposed method in the fault diagnosis framework was also verified through the use of experiments and simulations [14]. Conventional methods for PID controller parameter tuning require an accurate mathematical model of the controlled object, whereas fuzzy control and neural networks have strong adaptive and self-learning capabilities.