Nature-inspired Optimal Tuning of Scaling Factors of Mamdani Fuzzy Model for Intelligent Feed Dispensing System

Abstract

The increasing trends in intelligent control systems design has provide means for engineers to evolve robust and flexible means of adapting them to diverse applications. This tendency would reduce the challenges and complexity in bringing about the appropriate controllers to effect stability and efficient operations of industrial systems. This paper investigates the effect of two nature inspired algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), on PID controller for optimum tuning of a Fuzzy Logic Controller for Poultry Feed Dispensing Systems (PFDS). The Fuzzy Logic Controller was used to obtain a desired control speed for the conceptualized intelligent PFDS model. Both GA and PSO were compared to investigate which of the two algorithms could permit dynamic PFDS model to minimize feed wastage and reduce the alarming human involvement in dispensing poultry feeds majorly in the tropics. The modelling and simulation results obtained from the study using discrete event simulator and computational programming environment showed that PSO gave a much desired results for the optimally tuned FLC-PID, for stable intelligent PFDS with fast system response, rise time, and settling time compared to GA.