Design and Simulation of a Genetically Optimized Fuzzy Immune PID Controller for a Novel Grain Dryer

Controlling a grain dryer plant is a challenging task because of its complex drying mechanism. The aim of this paper is to investigate effective control strategies for a newly designed grain dryer plant, and to optimize the control performance, such as the overshoot, accuracy, and anti-disturbance abilities. In this paper, a genetically optimized fuzzy immune proportional integral derivative controller (GOFIP) is designed, which is a combination of intelligent fuzzy immune feedback control and traditional control. Fuzzy rules are used to imitate the biological immune feedback mechanism to automatically tune the proportional integral derivative (PID) parameters, and a genetic algorithm is used to optimize the controller's initial parameters, which can overcome the inadequacy of the general fuzzy immune PID controller. In addition, the dryer plant is introduced in this paper, and the classic drying model to verify the effectiveness of the proposed controller is fitted based on data from the practical drying experiments. Finally, simulation comparisons of control performances with three other controllers (the general PID controller, the fuzzy PID controller, and the fuzzy immune PID controller) are made, and anti-disturbance performance is tested based on the reformulated drying model in MATLAB. By simulating the step response of the outlet grain moisture content (MC), it is shown that the GOFIP controller has the best control performances to bring the outlet grain MC to the target value rapidly, enabling the drying control system to have no overshoot, better accuracy, and stronger anti-disturbance performance compared with the other three simulated controllers. The proposed controller has overcome the parameter adjustment difficulty of the traditional PID controller and can obtain the optimized control of grain drying by using the genetic optimization algorithm. The GOFIP controller is a reliable and precise control method incorporating uncertainty factors and may also provide an effective reference for controlling complex systems, such as those used in grain drying.