PID Control Widely Adopted in Industrial Production with Genetic Algorithm Implementation

PID control has been widely implemented across industrial production sectors, though obtaining optimal parameters remains one of the most challenging aspects of this control methodology. This example presents a genetic algorithm-based PID control approach for interested practitioners, where the algorithm mimics biological evolution processes to search for optimal PID parameter combinations. The implementation typically involves encoding PID parameters (Kp, Ki, Kd) as chromosomes, applying selection, crossover, and mutation operations across generations while evaluating fitness using performance metrics like ISE or IAE. The genetic algorithm approach to PID tuning has demonstrated effective results in numerous practical applications, making this a valuable reference for those seeking to deepen their understanding of advanced PID control techniques. Code implementation would typically include population initialization, fitness function definition based on system response criteria, and iterative optimization loops with convergence checks.