PID Tuning Algorithm (Genetic Algorithm Approach)

Resource Overview

Utilizing genetic algorithm toolbox for tuning and optimizing PID controller's three parameters, providing significant assistance for beginners in intelligent PID tuning optimization with practical implementation examples

Detailed Documentation

In this text, we demonstrate how to use the genetic algorithm toolbox to tune and optimize the three parameters (proportional, integral, and derivative) of a PID controller. Genetic algorithm is an optimization method inspired by biological evolution principles, which searches for optimal solutions by simulating natural selection processes including crossover and mutation operations. The implementation typically involves defining fitness functions that evaluate controller performance metrics such as overshoot, settling time, and steady-state error. Through this toolbox, beginners in intelligent PID tuning optimization can gain substantial assistance in understanding and mastering PID parameter adjustment methods. The code workflow generally includes population initialization, fitness evaluation, selection of elite individuals, genetic operations, and iterative optimization until convergence criteria are met.