# A New Method of Applying Data Engine Technology to Realize Neural Network Control
## 4 Motor Experimental Setup and Results
### Control Performance  
For a particular step signal, Figure 14 shows the control performance of the DRNN hybrid controller with different learning rates. It can be seen that the value of the learning rate on the control performance when the learning rate is small, the step response time longer, but it does not overshoot. However, when the learning rate increases gradually, the step response time is shorter, and the overshoot increases. In this experiment, when the learning rate is 0.02, it has the best control performance, which not only has short response time, but also no obvious overshoot.  
Based on the 0.02 optimal learning rate, we set up the disturbance experiment and the dynamic response experiment under the change of the reference model. Figure 15a shows the effect of a reference model change on motor speed control performance. When the controller reaches a steady state for a period, the weights have been trained and tend to converge. At this moment, the recognizer DRNI has adequately estimated the sensitivity information of the unknown motor device for the controller DRNC. Therefore, when the reference model changes from a sinusoidal signal to a constant signal, larger fluctuations do not appear. It should be noted that if the converted reference signal has a step characteristic (sharp increase in instantaneous error), we can observe from Figure 15b that the peak overshoot at the time of conversion and is quickly eliminated under the control of the DRNN controller.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Control Strategies** & **At 60 s–65 s** & **At 100 s–105 s** & **At 140 s–145 s** & **At 180 s–185 s** \\ \hline ANN & 1.09 RPM & 1.11 RPM & 1.03 RPM & 1.13 RPM \\ Conventional PI & 1.47 RPM & 1.59 RPM & 1.39 RPM & 1.29 RPM \\ \hline \hline \end{tabular}
\end{table}
Table 1: Error variance comparison between PI and neural controllers during the steady state operation.  
Figure 13: Reference tracking performance comparison between different control strategies, red line: Speed feedback value, green line: Reference model \(y_{r}(k)\), brown line: control signal \(y(k)\), blue line: Evolution of the errors for the rotational speed. (**a**) The initial control performance with RDNN; (**b**) The steady state control performance with RDNN; (**c**) The steady state control performance with PID.  
In the disturbance experiment, two-second disturbance bias signals were applied to the output of the equipment and the speed feedback respectively to simulate the disturbance of the actual control site. Experimental results are shown in Figure 16, a paranoid signal is applied at 160 s, the control system can quickly return the error signal to the weight vector calculation unit of the identifier DRNI for weight updating (the number of neurons in the DRNI network is set to 5), and adjust back to the steady state within 15 s. It shows that the neural network hybrid controller has good robustness in dealing with disturbance.  
Figure 14: Comparison of step control performance under different learning rates. (**a**) Performance curve when the learning rate is 0.01; (**b**) Performance curve when the learning rate is 0.02; (**c**) Performance curve when the learning rate is 0.03; (**d**) Performance curve when the learning rate is 0.04.  
Figure 15: Dynamic characteristics analysis of reference model switching condition. (**a**) Curve converted to line under continuous switching; (**b**) Non-continuous switching.