Static Friction Parameter Identification Using Genetic Algorithm

This article presents a method for identifying static friction parameters using a genetic algorithm, implemented in MATLAB 6.5. Genetic algorithms are heuristic search algorithms commonly employed for solving optimization problems. In this study, we utilize a genetic algorithm to identify static friction parameters, which is crucial for understanding and controlling friction phenomena. The implementation in MATLAB 6.5 leverages its powerful numerical computation capabilities through functions such as ga() for genetic algorithm optimization, where we define custom fitness functions to evaluate parameter accuracy against experimental data. The algorithm workflow includes population initialization, selection based on fitness scores, crossover operations using simulated binary crossover (SBX), and mutation with polynomial mutation operators. By executing this algorithm, we obtain precise friction parameters that provide deeper insights into friction behavior. Detailed implementation steps and results will be discussed in subsequent sections, including code snippets for parameter encoding (e.g., real-valued chromosomes representing friction coefficients) and convergence plots showing optimization progress. This method aims to support research and applications in related fields by offering a robust parameter identification framework.