# Research on the Autonomous Control Technology Used in the Slurry Mixing System of Cementing Units
## 3 Design of the Slurry Mixing System Controller
### Control Algorithm Simulation  
In the previous section, we detailed how the adaptive fuzzy PID controller was designed by analyzing the density control system. In this section, the controller model was built using Simulink, and simulation experiments were conducted. The comprehensive simulation of PID control, Smith control, and Smith fuzzy PID control was carried out using Simulink. Specifically, the PID control function was realized first; based on PID control, a compensation loop was introduced in parallel to form Smith control on the controller, and then, the Fuzzy logic control module was added to access the set fuzzy controller to achieve the Smith fuzzy PID function. The three control schemes output response images from the same oscilloscope. The controller wiring diagram built using Simulink is shown in Figure 6, and the internal structure of the fuzzy controller is shown in Figure 7.  
The input of the system was set as a step signal with an amplitude of one, the simulation time was 20 s, and the three initial parameters of the PID were \(K_{p}\) = 1000, \(K_{i}\) = 4, and \(K_{d}\) = 20. Through simulation analysis, it was found that there were significant differences in the response curves of the slurry density control system using conventional PID control, [PERSON] predictive compensation control, and [PERSON] fuzzy PID control under the condition that the PID parameters remain unchanged. The simulation results are shown in Figure 7.  
We set the input of the system to be a step signal with an amplitude of 1, the simulation time to be 20 s, and the three initial parameters of the PID to be \(K_{p}\) = 1000, \(K_{i}\) = 4, \(K_{d}\) = 20. It was found through the simulation analysis that, while keeping the PID parameters the same, the response curves of the slurry density control system obtained from conventional \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\(\epsilon\)} & \multicolumn{6}{c}{_ec_} \\ \cline{2-7}  & **NB** & **NM** & **NS** & **ZO** & **PS** & **PM** & **PB** \\ \hline NB & PB/NB/PS & PB/NB/NS & PM/NM/NB & PM/NM/NB & PS/NS/NB & ZO/ZO/NM & ZO/ZO/NM \\ NM & PB/NB/PS & PB/NB/NS & PM/NM/NM & PS/NS/NM & PS/NS/NM & ZO/ZO/NS & NS/ZO/ZO \\ NS & PM/NB/ZO & PM/NM/NS & PM/NS/NM & PS/NS/NM & ZO/ZO/NS & NS/PS/NS & NS/PS/ZO \\ ZO & PM/NM/ZO & PM/NM/NS & PS/NS/NM & ZO/ZO/NS & NS/PS/NS & NM/PM/NS & NM/PM/ZO \\ PS & PS/NM/ZO & PS/NS/ZO & ZO/ZO/ZO & NS/PS/ZO & NS/PS/ZO & NM/PM/ZO & NM/PB/ZO \\ PM & PS/ZO/PB & ZO/ZO/NS & NS/PS/PS & NM/PS/PS & NM/PM/PS & NM/PB/PS & NB/PB/PB \\ PB & ZO/ZO/PB & ZO/ZO/PM & NM/PS/PM & NM/PM/PM & NM/PM/PS & NB/PB/PS & NB/PB/PB \\ \hline \hline \end{tabular}
\end{table} 
Table 2: \(\Delta K_{p}\) fuzzy inference table. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Operating Parameter** & **Rise Time (s)** & **Peak Value** & **Overshoot** & **Stabilization Time (s)** \\ \hline Conventional PID Control & 1.875 & 1.36 & 35.6\% & 6.985 \\ Smith Predictive Control & 1.071 & 1.02 & 2. 1\% & 1.165 \\ Smith Fuzzy PID Control & 0.702 & 1.01 & 1. 3\% & 0.732 \\ GA Smith Fuzzy PID Control & 0.450 & 1.01 & 0. 9\% & 0.464 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Controller system performance indexes. PID control, [PERSON] predictive compensation control, and [PERSON] fuzzy PID control were significantly different from each other. The simulation results are shown in Figure 8.  
The dynamic simulation of the control system was performed using Simulink, and the simulation curves of conventional PID control, [PERSON] predictive compensation control, and [PERSON] fuzzy PID control were compared and analyzed. The following conclusions were drawn:  
(1) The conventional PID controller has a large overshoot and a long adjustment time. Figure 6: Controller simulation model. Figure 7: Internal structure diagram of the fuzzy controller. 2. Compared with the conventional PID control, [PERSON] predictive compensation control does not have any overshoot, and the stability of the system is greatly improved.