**Conflict of interest**

scripts. However, we find that this implementation would consume a rather long time, which is about 100 ms in our application. Since this delay is caused by the communication between MATLAB program in personal computer and LabVIEW program in NI CompactRIO, if we put the neural network into the CompactRIO directly, the delay could be eliminated entirely. Therefore, we complied the MATLAB code into a shared objects file (.so) which can be integrated to

*Results of neural network control of web tension and speed. The left figure shows the tension performance and*

CompactRIO directly. The resulted time to call the neural network is reduced to

**Figure 11** shows the results of using neural network to control web speed and tension. The reference speed and tension are set to 3 inch/second and 20 N, respectively. We have recorded the web tension and speed during the whole process. The maximum deviation (ΔT/T) of measured tension is 7 and 4% for speed (ΔV/V). The standard deviation is 0.2% for tension and 0.1% for speed. The tension

requirement in roll-to-roll fabrication is error within 10%. Thus, the neural network controller meets the requirements. Moreover, using neural network to control web speed and tension saves lots of work and time in identifying the mathematical motel of roll-to-roll system. We should mention that, during the starting phase, the variation of speed and tension is both larger than the other phases. The possible reason is that the training data from PID controller doesn't cover the region of interest in this phase, so that the interpolation of neural network is not accurate.

Roll-to-roll fabrication is known as a cost-effective method in producing electronic devices on flexible substrates. However, improper tension and speed may cause manufacturing defects of the substrate, including web wrinkling, edge cracks, and web misalignment, which lead to damages and wastes of the products. Hence, the study and control of web handling systems are carried out for decades. In this chapter, we introduce the two set of control algorithms in web handing field, model-based control and data-based control. In model-based control, a mathematical model of web tension and speed is derived. Based on the model, a robust H controller is applied. In data-based model, neural network control is discussed in detail. Two major learning methods are compared. A real application of neural network control in web handling is realized in roll-to-roll system. Both control algorithms have advantages and disadvantages. For model-based control, the

20 μm, which is fast enough for real-time application.

Our future work will include investigating this issue.

**4. Conclusion**

**222**

**Figure 11.**

*the right figure shows the speed performance.*

*Control Theory in Engineering*

The authors declared that they have no conflicts of interest to this work.
