This paper aims to present an alternative for modeling an inverse dynamic behaviors of a magneto-rheological (MR) damper using a Takagi-Sugeno-Kang (TSK) fuzzy inference system. The highly nonlinear dynamic nature of this device, however, has proven to be a significant challenge for researchers who try to characterize its behavior. Therefore, in this paper an optimum inverse TSK model of the MR dampers is developed using a meta-heuristic optimization algorithm to optimally emulate the nonlinear behavior of the MR dampers. Recently proposed grasshopper optimization algorithm (GOA) is selected as an optimization algorithm, and it is improved (IGOA) by adding opposition-based learning and merit function methods to boost its exploration and exploitation abilities. Also, IGOA is applied to tune the parameters exist in the TSK model. To investigate the efficiency of the proposed model, a nonlinear benchmark building under different far-field and near-field ground motions are considered, and results are compared with other control strategies such as clipped optimal controller (COC), passive ON, passive OFF and ANFIS. The results show that the proposed inverse TSK model of MR damper can provide very competitive results in comparison with other control algorithms.

本文旨在提出一种使用Takagi-Sugeno-Kang（TSK）模糊推理系统对磁流变（MR）阻尼器的逆动力学行为建模的替代方法。然而，对于试图表征其行为的研究人员而言，该设备的高度非线性动态特性已被证明是一项重大挑战。因此，在本文中，使用元启发式优化算法开发了MR阻尼器的最优逆TSK模型，以最佳地模拟MR阻尼器的非线性行为。选择了最近提出的蚱optimization优化算法（GOA）作为优化算法，并通过添加基于对立的学习和价值函数方法对其进行了改进（IGOA），以提高其勘探和开发能力。此外，IGOA应用于调整TSK模型中存在的参数。为了研究所提出模型的效率，考虑了在不同远场和近场地面运动下的非线性基准建筑物，并将结果与​​其他控制策略进行了比较，例如限幅最优控制器（COC），无源ON，无源OFF和ANFIS。结果表明，与其他控制算法相比，所提出的MR阻尼器TSK逆模型可以提供非常有竞争力的结果。