An inverse TSK model of MR damper for vibration control of nonlinear structures using an improved grasshopper optimization algorithm

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 metaheuristic 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.