Fuzzy Control Algorithm Implementation Based on Simulink

We will implement a fuzzy control algorithm design based on Simulink to address this problem. This algorithm utilizes fuzzy logic to simulate human decision-making processes, enabling better handling of system nonlinearities. During the design process, we will first construct a fuzzy inference system (FIS) that generates fuzzy outputs based on input variables and rule bases. The implementation involves defining membership functions for input/output variables and establishing fuzzy rules using MATLAB's Fuzzy Logic Toolbox. We will then employ fuzzy control rules to determine how to adjust output variables for achieving desired system behavior. To validate the algorithm's effectiveness, we will conduct multiple simulations and tests within Simulink environment, utilizing scope blocks for real-time monitoring and workspace exports for data analysis. Based on test results, we will iteratively refine and optimize the algorithm parameters through systematic tuning of membership functions and rule weights.