An intelligent robust controller, which combines a shuffled frog-leaping algorithm (SFLA) and an H∞ control strategy, is designed for a semi-active control system with magnetorheological (MR) dampers to reduce seismic responses of structures. Generally, the performance of mixed-sensitivity H∞ (MSH) control highly depends on expert experience in selecting the parameters of the weighting functions. In this study, as a recently-developed heuristic approach, a multi-objective SFLA with constraints is adopted to search for the optimal weighting functions. In the proposed semi-active control, firstly, based on the Bouc–Wen model, the forward dynamic characteristics of the MR damper are investigated through a series of tensile and compression experiments. Secondly, the MR damper inverse model is developed with an adaptive-network-based fuzzy inference system (ANFIS) technique. Finally, the SFLA-optimized MSH control approach integrated with the ANFIS inverse model is used to suppress the structural vibration. The simulation results for a three-story building model equipped with an MR damper verify that the proposed semi-active control method outperforms fuzzy control and two passive control methods. Besides, with the proposed strategy, the changes in structural parameters and earthquake excitations can be satisfactorily dealt with.

Finding effective means of protecting structures from dynamic hazards is a challenging task and has gained increasing significance. As for the seismically excited adjacent structures, an intelligent control strategy using magnetorheological dampers as connection devices considering soil–structure interaction is presented. First, the calculation model for the coupled structure–soil–structure interaction–magnetorheological damper system is developed, and the motion equation for calculating the seismic responses is then derived. Second, a semiactive control strategy integrating a modified crow search algorithm into a fuzzy logic control is proposed. In this strategy, to accurately calculate the voltage of magnetorheological dampers, the modified crow search algorithm with a hybrid coding strategy, priority selection scheme for target crows, new solution updating method, and guarantee mechanism of the solution feasibility is proposed to design the fuzzy logic control system. The numerical example of 10-story and 20-story coupled buildings demonstrates that soil–structure interaction should be taken into consideration to avoid overestimating the control effect. Besides, the proposed modified crow search algorithm outperforms genetic algorithm in terms of accuracy and robustness. Furthermore, by using magnetorheological dampers to interconnect the coupled structure with soil–structure interaction, dual advantages, that is response reduction and pounding mitigation can be achieved. The proposed modified crow search algorithm–fuzzy logic control method shows comprehensive performance superiority over its competitors, that is passive-off, passive-on, on–off, linear quadratic regulator–clipped voltage law, and linear quadratic Gaussian–clipped voltage law control strategies.

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Magnetorheological dampers have become prominent semi-active control devices for vibration mitigation of structures which are subjected to severe loads. However, the damping force cannot be controlled directly due to the inherent nonlinear characteristics of the magnetorheological dampers. Therefore, for fully exploiting the capabilities of the magnetorheological dampers, one of the challenging aspects is to develop an accurate inverse model which can appropriately predict the input voltage to control the damping force. In this article, a hybrid modeling strategy combining shuffled frogleaping algorithm and adaptive-network-based fuzzy inference system is proposed to model the inverse dynamic characteristics of the magnetorheological dampers for improving the modeling accuracy. The shuffled frog-leaping algorithm is employed to optimize the premise parameters of the adaptive-network-based fuzzy inference system while the consequent parameters are tuned by a least square estimation method, here known as shuffled frog-leaping algorithm-based adaptive-network-based fuzzy inference system approach. To evaluate the effectiveness of the proposed approach, the inverse modeling results based on the shuffled frog-leaping algorithm-based adaptive-network-based fuzzy inference system approach are compared with those based on the adaptive-network-based fuzzy inference system and genetic algorithm-based adaptive-network-based fuzzy inference system approaches. Analysis of variance test is carried out to statistically compare the performance of the proposed methods and the results demonstrate that the shuffled frogleaping algorithm-based adaptive-network-based fuzzy inference system strategy outperforms the other two methods in terms of modeling (training) accuracy and checking accuracy.