This article describes the application of a variant of the shark smell optimization (VSSO) biological inspired algorithm in the optimal design of a type-2 fuzzy controller. We show how the performance of VSSO is based on the frontal and rotational movement of the shark when navigating a dimensional search domain, which is based on the food-seeking behavior of sharks. The optimization of the design of a Mamdani interval type-2 fuzzy controller (IT2-FLC) applying VSSO is also described. The optimized controller is tested with the navigation of an autonomous mobile robot (AMR) in an unknown and changing environment. This work was developed as follows: first, the VSSO algorithm is improved by adjusting its main alpha and beta parameters with a fuzzy system, later the parameter values of the fuzzy controller input/output membership functions are optimized. Finally, a comparison is made between the results of type-1 (T1) and interval type-2 fuzzy controllers applying the proposed methodology. When comparing the T1 and IT2-FLC controllers, the application of the VSSO algorithm in T1-FLC shows good performance in robot navigation; however, IT2-FLC presents better performance due to its ability to handle higher levels of uncertainty. The performance evaluation of the proposed method and its application in different navigation problems has been carried out through computer simulations using Matlab-Simulink.

本文描述了鲨鱼气味优化 (VSSO) 生物启发算法的变体在 2 类模糊控制器的优化设计中的应用。我们展示了 VSSO 的性能如何基于鲨鱼在导航维度搜索域时的正面和旋转运动，这是基于鲨鱼的觅食行为。还描述了应用 VSSO 的 Mamdani 区间类型 2 模糊控制器 (IT2-FLC) 的设计优化。优化的控制器在未知和不断变化的环境中通过自主移动机器人 (AMR) 的导航进行测试。这项工作的发展如下：首先，通过使用模糊系统调整其主要 alpha 和 beta 参数来改进 VSSO 算法，随后对模糊控制器输入/输出隶属函数的参数值进行优化。最后，对应用所提出的方法的类型 1 (T1) 和区间类型 2 模糊控制器的结果进行了比较。在比较T1和IT2-FLC控制器时，VSSO算法在T1-FLC中的应用在机器人导航中表现出良好的性能；然而，IT2-FLC 表现出更好的性能，因为它能够处理更高级别的不确定性。所提出的方法的性能评估及其在不同导航问题中的应用已通过使用 Matlab-Simulink 的计算机模拟进行。VSSO算法在T1-FLC中的应用在机器人导航中表现出良好的性能；然而，IT2-FLC 表现出更好的性能，因为它能够处理更高级别的不确定性。所提出的方法的性能评估及其在不同导航问题中的应用已通过使用 Matlab-Simulink 的计算机模拟进行。VSSO算法在T1-FLC中的应用在机器人导航中表现出良好的性能；然而，IT2-FLC 表现出更好的性能，因为它能够处理更高级别的不确定性。所提出的方法的性能评估及其在不同导航问题中的应用已通过使用 Matlab-Simulink 的计算机模拟进行。