# Research on the Autonomous Control Technology Used in the Slurry Mixing System of Cementing Units
## 4 Optimization of the Slurry Mixing System Control Scheme
### Simulation of the Control Algorithm  
The [PERSON] fuzzy PID controller model was built according to the design scheme of the [PERSON] fuzzy PID controller optimized by the genetic algorithm proposed above, and the functions of the controller were realized using MATLAB. The Simulink wiring diagram is shown in Figure 11.  
The same simulation time and input values were used to simulate the slurry density system controlled by conventional PID control, [PERSON] predictive compensation control, [PERSON] fuzzy PID control, and the [PERSON] fuzzy PID controller optimized by the genetic algorithm, and the disturbance source was introduced simultaneously. The adaptation of the genetic algorithm reflects the error, overshoot, and adjustment time of the system. The adaptation curve is shown in Figure 12, and the simulation results of the four control methods are shown in Figure 13.  
It can be seen from the simulation results that when contending with interference, the system under the conventional PID controller produces certain oscillations, the adjustment time is the longest time measured, and the anti-interference ability is weak. Compared with conventional PID control, the control time of the [PERSON] predictive controller is relatively stable; however, the response time is still longer. The response of the [PERSON] fuzzy PID controller is more stable, the adjustment time is further reduced, and the control effect is more satisfactory. The [PERSON] fuzzy PID controller optimized by the genetic algorithm has the shortest stability time and can still show better processing performance, a faster  
Figure 11: Simulation structure of the [PERSON] fuzzy PID controller optimized by the genetic algorithm.  
Figure 12: Genetic algorithm adaptation curve.  
response speed, and a shorter stability time in the case of interference. It is therefore the best control scheme for the slurry density control system.  
Summarizing the output characteristics of the conventional PID control, [PERSON] predictive compensation control, [PERSON] fuzzy PID control, and [PERSON] fuzzy PID controller optimized by the genetic algorithm under step signals, we quantified the system performance indexes, as shown in Table 3.  
By analyzing the output curves of the four controllers and the system performance indexes, the following conclusions can be drawn:  
1. The conventional PID controller has a large overshoot, poor system stability, a long adjustment time, and poor anti-interference ability.
2. The [PERSON] predictive compensation control has a smoother curve, and it shows almost no obvious fluctuations and oscillations. Concurrently, it has a shorter rise time and can reach a stable state relatively faster; however, the response time is long.
3. Regarding the [PERSON] fuzzy PID control, the rise time is about 0.7 s, which means that it can reach the steady state quickly, and the overshoot is very small, measuring at around 0.32%, and the anti-interference ability is improved compared with the previous two methods.
4. Regarding the [PERSON] fuzzy PID control, the rise time is the shortest, around 0.3 s, the response speed is extremely fast, and its anti-interference ability is strong. Concurrently, the overshoot of the system is small and there is no steady-state error; therefore, the stability and dynamic performance of the system are better.  
After comparing the four different control schemes, the Smith fuzzy PID control optimized by the genetic algorithm was selected as the control scheme for the slurry mixing time-delay system. This scheme achieved the shortest rise time, with the system therefore being able to reach stability quickly. In addition, it effectively eliminates the oscillation and overshoot caused by the time delays of the system and it will not generate steady-state errors after stabilization, which greatly improves the overall performance of the system.