## 5 Practical Implementations in Engineering Applications  
At present, the hardware controller of wind power systems mainly adopts a PLC system, which is also due to the reliability and wide adaptability of PLCs in analog process control, sequence control, and various motor system control applications. For example, the aforementioned lead motor and auxiliary motor can be easily controlled by the PLC controller system. In particular, in the simulation results in Figure 5, we can see that the two control input signals from the motors are more suitable for control implementations provided by the stepping motors. The PLC has built-in driving modules for various stepping motors, which can reliably and effectively drive the stepping motors.  
Practical Implementation of the PD-Based Iterative Learning Algorithm by Using C-HITL Simulation Technology  
In the present study, a virtual simulation platform was proposed to verify the feasibility of the controller hardware implementation for control algorithms. This was a platform using C-HITL simulation technology based on OPC communication [30]. To avoid the failure of the algorithm in the hardware implementation process due to the problem of hardware memory limitation and the cumulative error of calculations in the PLC hardware just mentioned, the C-HITL simulation was used to verify the feasibility of the hardware implementation of the control algorithm. For the software-in-the-loop simulation method and the HITL simulation technology [26; 30] mentioned above, using the C-HITL platform, we not only demonstrated the feasibility of the software implementation of complex control algorithms, but also demonstrated the feasibility of the implementation of complex control algorithms in controller hardware, providing a feasibility analysis and reference for engineering applications.  
In the C-HITL platform, the PLC CPU module runs the entire PD algorithm by using a built-in PD controller, sends the output signals to the PC to drive the governing system, Equation (12), and receives signals from the PC. The system model and iterative algorithm are run in the MATLAB/SIMULINK (R2022b, MathWorks, Natick, MA, USA) environment in the PC. Sending and receiving are performed through the OPC mechanism. OPC Read/Write blocks in SIMULINK can read or write signals from or into the PLC system by the OPC server.  
The signals of the controlled displacements (\(s\) and \(\theta\)) and the practical control inputs (\(u_{1}\) and \(u_{2}\)) can be displayed by a human machine interface (HMI) connected to the PLC CPU. Figure 8a shows the C-HITL experiment plan based on OPC communication. Figure 8b demonstrates the practical control inputs (\(u_{1}\) and \(u_{2}\) in HMI) denoted by \(u_{1}\) (blue mark) and \(u_{2}\) (red mark) under the same simulation conditions mentioned in Sections 4.1 and 4.2.  
The HMI in Figure 8b shows the process of dynamic fluctuations in the control input signals. The control inputs, i.e., control moments in Figures 5 and 8b, demonstrate almost the same changing trends and amplitudes of the control moments, and show a picture frame within time range [0 30s]. The numerical simulation and C-HITL experiment implementation show perfect consistency. As for the smoothness of the fluctuating curve in Figure 8b, it looks somewhat different from Figure 5 because the sampling time of the PLC hardware system (including the HMI display) is different from that of the simulation method in MATLAB. It should be noted that the HMI not only displays the control input signals, but also displays the real-time fluctuation processes of the controlled signals, which also shows the effectiveness of the C-HITL platform based on OPC technology, and further demonstrates the feasibility of the iterative algorithm to achieve engineering applications in the controller hardware.