Hybrid Design Optimization of Heating Furnace Temperature using ANFIS-PSO

Abstract

-- Intelligent control design for industrial heating furnace temperature control is indispensable. PID, Fuzzy, and ANFIS controllers have been proven reliable and have been widely used. However, it is constrained in choosing a better gain controller. Then an approach method is given to determine the most appropriate controller gain value using the artificial intelligence tuning method. The artificial intelligence method used is a combination of the Adaptive Neuro Fuzzy Inference System and Particle Swarm Optimization (ANFIS-PSO) methods. As a comparison, several methods were used, namely; Conventional PID (PID-Konv), Matlab Auto tuning PID (PID-Auto), PSO tuned PID (PID-PSO), and Hybrid ANFIS-PSO. The ANFIS-PSO controller is the best choice compared to conventional single loop control systems, conventional PID, and matlab 2013a auto tuning methods to control this nonlinear process. The simulation results show that the ANFIS-PSO design is the best method with overshot = 0.0722, undershot 0.0085, and settling time at 18.8789 seconds which can produce a fast response with strong dynamic performance.