Semi-Active Vibration Control of a Non-Collocated Civil Structure Using Evolutionary-Based BELBIC

Abstract

A buildings resilience to seismic activity can be increased by providing ways for the structure to dynamically counteract the effect of the Earth’s crust movements. This ability is fundamental in certain regions of the globe, where earthquakes are more frequent, and can be achieved using different strategies. State-of-the-art anti-seismic buildings have, embedded on their structure, mostly passive actuators such as base isolation, Tuned Mass Dampers (TMD) and viscous dampers that can be used to reduce the effect of seismic or even wind induced vibrations. The main disadvantage of this type of building vibration reduction strategies concerns their inability to adapt their properties in accordance to both the excitation signal or structural behaviour. This adaption capability can be promoted by adding to the building active type actuators operating under a closed-loop. However, these systems are substantially larger than passive type solutions and require a considerable amount of energy that may not be available during a severe earthquake due to power grid failure. An intermediate solution between these two extremes is the introduction of semi-active actuators such as magneto–rheological dampers. The inclusion of magneto–rheological actuators is among one of the most promising semi-active techniques. However, the overall performance of this strategy depends on several aspects such as the actuators number and location within the structure and the vibration sensors network. It can be the case where the installation leads to a non-collocated system which presents additional challenges to control. This paper proposes to tackle the problem of controlling the vibration of a non-collocated three-storey building by means of a brain–emotional controller tuned using an evolutionary algorithm. This controller will be used to adjust the stiffness coefficient of a magneto–rheological actuator such that the building’s frame oscillation under earthquake excitation, is mitigated. The obtained results suggest that, using this control strategy, it is possible to reduce the building vibration to secure levels.

Minimum Energy Control of Quadrotor UAV: Synthesis and Performance Analysis of Control System with Neurobiologically Inspired Intelligent Controller (BELBIC)

“…In [34], implementation of the intelligent adaptive controller for an electrohydraulic servo system based on the brain emotional learning (BEL) mechanism was presented. Joao Paulo Coelho et al adapted BELBIC controllers into two control systems: (a) one with a mathematical model of magneto-rheological (MR) damper [35], and (b) one with a model of a non-collocated three-story building with MR [36], respectively. Lucas Caro et al applied an intelligent controller to the neurofuzzy microheat exchanger model in [37,38] to control the laboratory overhead travel crane in a model-free and embedded manner.…”

Research developments in adaptive intelligent vibration control of smart civil structures

“…Their proposed PSO optimized controller has shown higher performance in achieving control objectives. Braz César et al 83 presented a BELBIC combined with PSO algorithms for the seismic vibration control of a 3-storey building structure equipped with MR damper. The simulation results presented the high efficiency of proposed schemes in regards to reducing the overall structural response.…”

“…PSO applications. Braz César et al 83 incorporated PSO for tuning the parameters of a BELBIC utilized for the seismic vibration control of 3-storey building structure equipped with MR damper. The simulation results present the high efficiency of proposed schemes in reducing the overall structural response.…”

Semi-active vibration control of building structure by Self Tuned Brain Emotional Learning Based Intelligent Controller

“…There are a few methods for tuning the parameters of BELBIC. These methods include the trial-and-error method [33], or are based on the result of a cost function optimization using evolutionarybased algorithms (particle swarm optimization (PSO) algorithm [34][35][36][37][38] and clonal selection algorithm) [40] and Lyapunov based algorithm [41]. Even some recent research utilises intelligent techniques such as fuzzy logic to tune its parameters [42,43].…”

“…As César et al [36], presented a study of seismic vibration control of SDOF building structure equipped with MR damper where the authors have utilized the capabilities of PSO for the tuning of BELBIC, Rew and SI parameters. One more study was reported by Braz César et al [35], in which a threestory seismically excited building equipped with MR damper have been controlled by PSO optimized BELBIC. Both of these investigations used offline tuning techniques, as previously mentioned.…”