Fuzzy logic control is well known for its robustness across various automation engineering applications. Its structured linguistic modeling of human expertise and empirical knowledge makes it attractive to both students and control engineers. However, such expertise can only be developed through practice and trials. Existing solutions are time‐ and energy‐consuming general‐purpose software, as the literature lacks ready‐to‐use tools dedicated to this purpose. Therefore, this paper introduces P4FPID, a novel fuzzy logic toolbox for direct and advanced interaction with fuzzy controllers. Developed using Processing 4.4, P4FPID adopts innovative graphical human‐computer interaction paradigms that allow students to smoothly configure and experiment with fuzzy controllers in real time through simulation, animation, or hardware integration. Owing to its dynamic simulation model, P4FPID enables immediate observation of system feedback. The proposed software also supports fuzzy set subnormality exploration (an aspect rarely addressed in fuzzy control systems) and integrates an interactive genetic algorithm capable of animating more than 20 optimization scenarios. In addition, it supports industrial PLC standards (IEC 61131) and MATLAB file formats, making it suitable for both academic and professional use. As a case study application, P4FPID integrates a digital model of a Ball‐and‐Beam system currently in use, enabling experimentation across a wide range of benchmark scenarios. In this paper, a user experience survey showed a clear preference for P4FPID over the MATLAB Fuzzy Logic Toolbox, while benchmark experiments against MATLAB/Simulink & Python Simpful highlighted the advantages of an application‐oriented software approach over general‐purpose tools in terms of execution time.
No takes yet. Share an insight, caveat, or question.
Boutalbi et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: