# A New Method of Applying Data Engine Technology to Realize Neural Network Control
## 4 Motor Experimental Setup and Results  
### Experimental Setup  
The proposed DRNN controller validation framework was applied to real motor speed measurement and control experiment platform environmental modeling case studies to demonstrate the scientific DE technology that is used in industrial applications of neural network control. A snapshot of the experimental setup is illustrated in Figure 10; it shows the architecture is divided into engineering station (Human-Machine Monitoring layer), control layer, executive layer, and application layer. In this paper, IPC (IPC-S2221) based on QNX control system is used as a controller to run the DRNN control algorithm logic through the embedded DE software, and realize the self-iterative continuous operation of the algorithm components in the form of data update, and then generate the final control signal through the shared memory sent to the corresponding output component of the PLC that is the executing device, by the PLC CS1W-DA08C analog output module output to the voltage input end of the TaiDa servo driver for control.  
Figure 10: Components of the experimental setup. 1: Router, 2: IPC, 3: 10V and 5V power supply, 4: Servo motor, 5: Optical electrical rotary encoder, 6: Server driver, 7: PLC, 8: D/A channels, 9: Engineer station, 10: Oscilloscope.  
Figure 9: Convergence of the DRNN weights for the reference mode: (**a**) DRNC, (**b**) DRNI.  
The feedback thread of the control loop is used as the speed sensor by the Omron C6B2-C optical-electrical rotary encoder converts the geometric displacement of the servomotor into pulse digital, the resolution of the encoder is set to 60P/R, that is used to simulate the proximity switch signal in actual industrial applications. A PLC CS1W-CT021 high-speed counting module was used for acquisition which the maximum input frequency is 500 kHz, in order to carry out a real-time testing. The speed is calculated by the DE software embedded in Omron PLC and then fed back to IPC for neural network control algorithm iterative operation, eventually forming closed-loop control. The D/A channels are used to provide the command signal outputs to be shown in the oscilloscope.  
The structure of the entire control system and the flowchart of control information are shown in Figure 11, through the engineer station, the corresponding version of the DE software downloaded to IPC and PLC respectively to build a unified memory real-time sharing mechanism. The application of a unified graphical configuration component as a programming language, effectively solves the problem of integrated control of heterogeneous devices. This article is mainly used in the collaborative control of Omron PLC and SuoQi IPC, which fully combines the reliability and stability of PLC as the actuator and the powerful computing ability of the IPC, which effectively improves the anti-disturbance ability of the control system and the implementation efficiency of nerve network control algorithm. From the cost analysis, it not only reduces the cost of separately developing the ladder diagram of the PLC and developing the IPC based on C #, Java, and XML, but also the implementation process of the algorithm is transparent that convenient for engineers to carry out unified debugging under the condition of online operation, reducing the running risk of the system. The logic diagram of the coordinated control configuration is shown in Figure 12 below. Also, the entire system is based on Industrial Ethernet communication, which has good real-time function and stability on data transmission and reduces the control delay error caused by communication delay.  
Figure 11: Block diagram of DRNN closed-loop compound control system based on DE Theory. AO: Output analog component, AI: Input analog component, SM: Speed measurement component.