# A New Method of Applying Data Engine Technology to Realize Neural Network Control
## 4 Motor Experimental Setup and Results
### Dynamic Reconstruction Performance  
To reflect the adaptability and high availability of DRNN hybrid controller in an unintended environment, a unique concept of dynamic reconfiguration based on DE technology was introduced in this paper, which is a synonym of runtime evolution, the ability to change a system's functionality and topology while it is running. The benefit is application consistency and service continuity for the system while it is being updated [35; 36]. One of the significant features of the controller based on the DE technology is that the control logic topology is written by the host computer and then downloaded to different lower-level machines for execution control, and the data of control execution devices are interconnected and interoperable under the mechanism of shared memory, the data transformation process of each data unit can be monitored on the host computer in real time. Therefore, when an extreme condition such as disconnection or a crash occurs on the engineer station in the host computer, it will not affect the implementation of IPC and PLC on the control strategy, when the host computer resumes grid connection, it can be monitored immediately. As shown in Figure 17a,b, the engineer station (network disconnected) was shut down manually at 120 s, and the engineer station (connected to the grid) was restarted at 200 s, the control system can still operate in the steady state and no interference appears. Besides, the neural network hybrid controller can be switched online without disturbance. As can be seen from Figure 17c, when changing the control mode from conventional PID control to DRNN control, there is no significant dynamic tracking error similar to the one shown in Figure 13a that results from the convergence of the weight update during the training start-up phase, but only a very small range of overshoot fluctuations. This is because, in the process of PID control, we also study the DRNN control logic online at the same time. When the error value is smaller than certain smaller value, we can download the topology of the DRNN control logic to the control station to achieve interference-free switch. It should be noted that the online learning samples are from the reference model, so the ideal learning environment generated feedback error value is small, and in the moment  
Figure 16: Dynamic characteristics analysis of disturbance condition. (**a**) Adaptive curve under equipment output disturbance; (**b**) Weight update curve under equipment output disturbance; (**c**) Adaptive curve under feedback signal disturbance; (**d**) Weight update curve under feedback signal disturbance. of switching, the learning sample is transformed into the measured value of the actual motor speed, resulting in a sudden change in the error feedback signal, which is also the main reason for the small overshoot. As shown in Figure 17d, the weight update trend shows that the weight update curve is very smooth before switching. After switching to the DRNN control logic, the updating process of weights is adjusted in real time with the fluctuation of feedback errors, showing a certain degree of volatility. In order to further study the dynamic reconfiguration of the controller, this paper set a group of experiments by modifying the characteristics of the controlled motor. In this paper, we superimpose a single-input single-output difference equation on an unknown controlled object, and try to dynamically modify the characteristic equation of the motor online to observe the performance of the DRNN hybrid  
Figure 17: Dynamic characteristics analysis curves under control mode switching between advanced control and normal control.