Hairpin RNA genetic algorithm based ANFIS for modeling overhead cranes

• Published in: Mechanical systems and signal processing Vol. 165; p. 108326
• Main Authors: Zhu, Xiaohua, Wang, Ning
• Format: Journal Article
• Language: English
• Published: Berlin Elsevier Ltd 15.02.2022; Elsevier BV
• Subjects: Adaptive network-based fuzzy inference system (ANFIS); Control systems design; Cranes; Genetic algorithms; Hairpin crossover operator; Hairpin mutation operator; Mathematical models; Molecular structure; Operators (mathematics); Optimization; Overhead crane modeling; Parameters; Ribonucleic acid; RNA; RNA genetic algorithm; RNA genetic algorithm; Hairpin mutation operator; Hairpin crossover operator; Adaptive network-based fuzzy inference system (ANFIS); Overhead crane modeling
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2021.108326

•A novel RNA genetic algorithm (hRNA-GA) is proposed.•The hairpin crossover operator and the hairpin mutation operator are designed.•The hRNA-GA is applied to find the optimal premise and consequent parameters of ANFIS models.•The hRNA-GA based ANFISs for modeling the overhead crane reaches good performance. Obtaining an accurate mathematical model is an important subject to design an overhead crane control system. However, there are some deviations between an existing model and a physical system due to its nonlinearity and complexity characteristics. Motivated by this fact, an adaptive network-based fuzzy inference system (ANFIS) modeling method is proposed for obtaining high precision models. One of the challenges in ANFIS modeling is how to effectively optimize the premise and consequent parameters. To solve this problem, we propose the RNA genetic algorithm with hairpin genetic operators (hRNA-GA). In hRNA-GA, inspired by the hairpin structure in RNA molecules, we design the hairpin crossover operator and the hairpin mutation operator to maintain the population diversity and avoid the premature convergence. Numerical experiments have been conducted on some benchmark functions. The results indicate that hRNA-GA has better search ability with respect to quality and stability of solutions. Finally, hRNA-GA is applied to find the optimal parameters of ANFISs for modeling an actual overhead crane system and the satisfactory results are reached.