Gajate Agustín, Haber Rodolfo E, Vega Pastora I, Alique José R
Centro de Automática y Robótica, Consejo Superior de Investigaciones Científicas, Madrid 28500, Spain.
IEEE Trans Neural Netw. 2010 Jul;21(7):1158-67. doi: 10.1109/TNN.2010.2050602.
Recently, new neuro-fuzzy inference algorithms have been developed to deal with the time-varying behavior and uncertainty of many complex systems. This paper presents the design and application of a novel transductive neuro-fuzzy inference method to control force in a high-performance drilling process. The main goal is to study, analyze, and verify the behavior of a transductive neuro-fuzzy inference system for controlling this complex process, specifically addressing the dynamic modeling, computational efficiency, and viability of the real-time application of this algorithm as well as assessing the topology of the neuro-fuzzy system (e.g., number of clusters, number of rules). A transductive reasoning method is used to create local neuro-fuzzy models for each input/output data set in a case study. The direct and inverse dynamics of a complex process are modeled using this strategy. The synergies among fuzzy, neural, and transductive strategies are then exploited to deal with process complexity and uncertainty through the application of the neuro-fuzzy models within an internal model control (IMC) scheme. A comparative study is made of the adaptive neuro-fuzzy inference system (ANFIS) and the suggested method inspired in a transductive neuro-fuzzy inference strategy. The two neuro-fuzzy strategies are evaluated in a real drilling force control problem. The experimental results demonstrated that the transductive neuro-fuzzy control system provides a good transient response (without overshoot) and better error-based performance indices than the ANFIS-based control system. In particular, the IMC system based on a transductive neuro-fuzzy inference approach reduces the influence of the increase in cutting force that occurs as the drill depth increases, reducing the risk of rapid tool wear and catastrophic tool breakage.
最近，已开发出新的神经模糊推理算法来处理许多复杂系统的时变行为和不确定性。本文提出了一种新颖的直推式神经模糊推理方法的设计与应用，用于控制高性能钻孔过程中的力。主要目标是研究、分析和验证直推式神经模糊推理系统在控制这一复杂过程中的行为，具体解决该算法实时应用的动态建模、计算效率和可行性问题，以及评估神经模糊系统的拓扑结构（如聚类数量、规则数量）。在一个案例研究中，使用直推式推理方法为每个输入/输出数据集创建局部神经模糊模型。采用该策略对复杂过程的正向和逆向动力学进行建模。然后，通过在内部模型控制（IMC）方案中应用神经模糊模型，利用模糊、神经和直推式策略之间的协同作用来处理过程的复杂性和不确定性。对自适应神经模糊推理系统（ANFIS）和受直推式神经模糊推理策略启发的建议方法进行了比较研究。在实际的钻孔力控制问题中对这两种神经模糊策略进行了评估。实验结果表明，直推式神经模糊控制系统提供了良好的瞬态响应（无超调），并且比基于ANFIS的控制系统具有更好的基于误差的性能指标。特别是，基于直推式神经模糊推理方法的IMC系统减少了随着钻孔深度增加而出现的切削力增加的影响，降低了刀具快速磨损和灾难性刀具破损的风险。
