# A Program Model of Fuzzy Interpreted Petri Net to Control Discrete Event Systems
## 2 Comparison to Related Works  
In this section, the authors want to raise the issue related to executable specification [54; 55; 56; 57; 58; 59; 60]. A graphical representation based on FIPN created in FIPN-SML allows the presentation of the control system behaviour, validation of this system before it is implemented, and clarification of the requirements (that may be initially unclear). This solution enables the automatic construction of executable code and offers a higher abstraction of the designed system. It also reduces the costs and the time needed to develop control systems. All of these advantages are analogical to the cited executable specifications. Moreover, there are some similar approaches to the one proposed in this work [28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 41]. Implementation of some of them can also be seen as a programming language. They all have unquestionable benefits. Nevertheless, the authors believe that their solution can be characterized by some unique advantages. Based on [51], in literature, fuzzy nets are mainly used to create expert systems, but few deal with the direct application to control [61; 62; 63]. These approaches do not have software tool support, do not permit modelling of resources using the net structure, nor do they generate executable code automatically.  
When compared to other mentioned solutions [28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 41], the main advantage of the program model based on FIPN is the possibility of using analogue sensors for the direct control of DES. Contrary to Grafcet and SFC [28; 29; 30; 31; 32; 33], FIPN allows resources modelling in the structure of a net, because the weight of arcs and the places capacity can be greater than one, while Grafcet and SFC are based on binary net. Both Grafcet and SFC have similar features to FIPN. They both enable simulation of the created system before it is implemented in PLC and automatic executable code generation based on the simulation model (the graphical representation). However, Grafcet and SFC have some advantages compared to FIPN. Their main advantage is the use of a modularity/hierarchical structure. The authors are aware of this limitation of FIPN and their work related to this subject is currently under consideration by another journal.  
Another solution is SIPN [35; 36; 37], a binary net, with a formal description. Transformation of the graphical representation to executable code is informal in the examples (IL and LD). As with FIPN, SIPN permits automatic generation of executable code. The main advantages in comparison with FIPN are the automatic investigation of properties through application of the SIPN editor and the possibility of using modularity. Apart from the three solutions mentioned, which seem complete because of software tools, there are some others which propose the conversion of PNs to LD [34; 38; 39; 41]. They have many advantages and they do not take into consideration only binary nets, (e.g., [34]). However, they do not allow automatic generation of executable code based on the graphical representation of a net using a software tool.  
To conclude this section, it can be observed that there is no other solution that combines PNs and PLC programming to create control systems and offer the possibility of using analogue sensors, software tool support, resources modelling by the structure of the net, and automatic generation of executable code. However, some new functionalities need to be implemented to see FIPN-SML as a complete solution.