An AI based particle swarm optimization control for vibration reduction of a space framed structure

The study aimed at developing an adaptive Linear Quadratic Gaussian Design with artificial intelligence based Particle Swarm Optimization (LQG-PSO) mechanism. The purpose of the proposed mechanism is to reduce the dynamic vibration of a space framed structure subjected to seismic loads. The addressed damper force has been measured and provided using a developed MR damper ARX model. A framed structure has been adopted as a benchmark study model to illustrate the performance of the proposed algorithm. Various time histories data has been used as input to the framed structure. For the benchmark frame problem, the proposed method has been found to be more significant in reducing the floor vibration responses in comparison with the conventional Linear Quadratic Gaussian (LQG) design method and passive control method with tuned liquid column damper. The proposed artificial intelligence based control method with the designated MR damper model is effective in calculating the optimized control force required from MR damper. Therefore, it minimizes the need of dampers capable of attaining higher damper force and the number of dampers. In consequence, it reduces the structural vibration reduction and the cost of maintenance during earthquakes.