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.

获得准确的数学模型是设计桥式起重机控制系统的重要课题。然而，现有模型由于其非线性和复杂性特征，与物理系统存在一定的偏差。基于这一事实，提出了一种基于自适应网络的模糊推理系统（ANFIS）建模方法来获得高精度模型。ANFIS 建模的挑战之一是如何有效地优化前提和后续参数。为了解决这个问题，我们提出了带有发夹遗传算子的RNA遗传算法（hRNA-GA）。在hRNA-GA中，受RNA分子中发夹结构的启发，我们设计了发夹交叉算子和发夹变异算子，以保持种群多样性并避免早熟收敛。已经对一些基准函数进行了数值实验。结果表明hRNA-GA在溶液的质量和稳定性方面具有更好的搜索能力。最后，应用 hRNA-GA 寻找 ANFIS 的最佳参数，以对实际桥式起重机系统进行建模，并取得了令人满意的结果。