Emotional Learning based adaptive control algorithm for Semi-Active seismic control of structures

Abstract

A tuned mass damper (TMD) is a passive mass-spring system, widely accepted as an effective response control strategy for a structure under external excitation. The inherent passive nature, optimum tuning requirements of TMDs are known to lead to sensitivity problems and/or detuning of the TMDs for changing vibration conditions, limiting their performance to narrow band of input excitation. Due to these limitations, semi-active control concepts are used to introduce adaptability in passive TMDs. Semi-active response control using TMD is much researched in recent years for protection of civil engineering structures against earthquake. In this study, the efficacy of a semi-active mass driver (MD) system coupled with magnetorheological (MR) damper is investigated for seismic response control of a primary structure. The input current to the MR damper in the semi-active MR-MD is controlled using an adaptive Brain Emotional Learning Based Intelligent Controller (BELBIC) control algorithm in a closed loop architecture. The efficiency of the damping addition from the MR damper to the mass driver which is reducing the response of the primary structure is investigated using analytical and simulation studies, considering off-tuning, emulated using three different tuning cases of the MR-MD. From the studies, it is found that semi-active MR-MD is performing satisfactorily in reducing the seismic response of the primary structure and BELBIC algorithm performs efficiently even during off-tuning conditions and presence of physical constraints.