SELF-LEARNING PID TEMPERATURE CONTROLLER USING GENETIC ALGORITHM FOR DYNAMIC ENVIRONMENTS

Abstract

Industrial systems use heat exchangers to manage their temperature because these systems operate through nonlinear processes that experience changing conditions while facing external interruptions. The extensive use of PID controllers results from their easy implementation, but these controllers face performance challenges when system dynamics change because their static gain settings cannot match different operational conditions. Current research indicates that inadequate tuning results in prolonged settling time and heightened peak overshoot, hence diminishing system efficiency. The research develops a self-learning PID temperature controller which uses a Genetic Algorithm (GA) for real-time dynamic environment adaptive tuning. The method provides continuous online learning, which enables the controller to operate without interruption while it adjusts to changes in environmental conditions that include variable loads and disturbances and nonlinear behaviour, unlike conventional GA-PID methods which perform parameter optimisation through offline processes. The system uses PID control, which allows genetic algorithm operators to dynamically adjust proportional and integral and derivative gains through their selection and crossover and mutation processes. The controller uses inaccuracy and overshoot and settling time to evaluate performance metrics as fitness functions. The proposed model improves flexibility by incorporating real-time feedback-driven optimisation loops, drawing inspiration from previous GA-PID implementations in heat exchanger systems (diagrams on pages 3–15). The dynamic temperature fluctuations will be simulated through MATLAB/Simulink and the controller effectiveness will be evaluated through this modelling approach. The proposed self-learning GA-PID controller is expected to achieve better performance because it will decrease settling time and peak overshoot more effectively than existing traditional GA-tuned PID systems which use static tuning methods. The paper presents a comprehensive, adaptive temperature management system designed for intricate industrial settings, enhancing stability, efficiency, and real-time responsiveness compared to conventional control methods.