PID Tuning Using Genetic Algorithm

Genetic Algorithm PID Tuning Problem: Given system parameters K=1 and T=2, genetic algorithm is employed to optimize PID parameters. The implementation supports both binary and real-valued encoding schemes without bit-length restrictions. Key genetic algorithm parameters including population size (M), crossover probability (Pc), and mutation probability (Pm) can be customized. Performance evaluation uses a specified metric with Q=100 simulation steps. During optimization, the genetic algorithm iteratively improves PID parameters by maximizing fitness function values through selection, crossover, and mutation operations. The algorithm systematically explores the parameter space to identify optimal combinations, ultimately enhancing control system accuracy and dynamic performance. Code implementation typically involves fitness calculation based on system response error (e.g., ITAE, ISE metrics) and genetic operators like tournament selection, uniform crossover, and Gaussian mutation for real-coded chromosomes.