Hairpin RNA genetic algorithm based ANFIS for modeling overhead cranes

Abstract 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.